{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "9d577811",
      "metadata": {
        "id": "9d577811"
      },
      "source": [
        "# Artificial Intelligence for Credit Risk Assessment\n",
        "\n",
        "## Module Information\n",
        "**Module Code:** ACCA7026\n",
        "\n",
        "**Module Name:** Artificial Intelligence\n",
        "\n",
        "**Assessment:** Assignment – ML Model\n",
        "\n",
        "**Student Name:** Divyesh Dudhat\n",
        "\n",
        "**Student ID:** 2529493\n",
        "\n",
        "**Dataset:** Lending Club Loan Data (loans_full_schema.csv)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "81e2dabe",
      "metadata": {
        "id": "81e2dabe"
      },
      "source": [
        "# Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eb933cd6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eb933cd6",
        "outputId": "a66601db-526d-4f3c-d296-115c421eacbf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Libraries imported successfully.\n"
          ]
        }
      ],
      "source": [
        "# Core data handling\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Visualisation\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Preprocessing and modelling\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Evaluation\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        "    f1_score,\n",
        "    roc_auc_score,\n",
        "    roc_curve,\n",
        "    confusion_matrix,\n",
        "    classification_report\n",
        ")\n",
        "\n",
        "# Explainability\n",
        "import shap\n",
        "\n",
        "# General settings\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "sns.set_style(\"whitegrid\")\n",
        "plt.rcParams[\"figure.figsize\"] = (8, 5)\n",
        "\n",
        "RANDOM_STATE = 42\n",
        "\n",
        "print(\"Libraries imported successfully.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e4c28f9f",
      "metadata": {
        "id": "e4c28f9f"
      },
      "source": [
        "# Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f00f360f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f00f360f",
        "outputId": "c79d2d15-a827-4485-85e3-28b1109fc21d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset shape (rows, columns): (10000, 56)\n"
          ]
        }
      ],
      "source": [
        "# Load the dataset\n",
        "df = pd.read_csv(\"/content/loans_full_schema.csv\")\n",
        "\n",
        "# Basic dimensions\n",
        "print(\"Dataset shape (rows, columns):\", df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "61f426ee",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "61f426ee",
        "outputId": "2c202405-68de-4216-bc34-a1bed338cb83"
      },
      "outputs": [
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              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
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              "      <th>annual_income</th>\n",
              "      <th>verified_income</th>\n",
              "      <th>debt_to_income</th>\n",
              "      <th>annual_income_joint</th>\n",
              "      <th>verification_income_joint</th>\n",
              "      <th>...</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>issue_month</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>initial_listing_status</th>\n",
              "      <th>disbursement_method</th>\n",
              "      <th>balance</th>\n",
              "      <th>paid_total</th>\n",
              "      <th>paid_principal</th>\n",
              "      <th>paid_interest</th>\n",
              "      <th>paid_late_fees</th>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
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              "      <td>global config engineer</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NJ</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>90000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>18.01</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>C3</td>\n",
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              "      <td>1999.33</td>\n",
              "      <td>984.14</td>\n",
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              "      <td>0.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>warehouse office clerk</td>\n",
              "      <td>10.0</td>\n",
              "      <td>HI</td>\n",
              "      <td>RENT</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>5.04</td>\n",
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              "      <td>150.49</td>\n",
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              "      <th>2</th>\n",
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              "      <td>assembly</td>\n",
              "      <td>3.0</td>\n",
              "      <td>WI</td>\n",
              "      <td>RENT</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>21.15</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Cash</td>\n",
              "      <td>1824.63</td>\n",
              "      <td>281.80</td>\n",
              "      <td>175.37</td>\n",
              "      <td>106.43</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>customer service</td>\n",
              "      <td>1.0</td>\n",
              "      <td>PA</td>\n",
              "      <td>RENT</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>10.16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>A3</td>\n",
              "      <td>Jan-2018</td>\n",
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              "      <td>whole</td>\n",
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              "      <td>3312.89</td>\n",
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              "      <td>566.15</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>security supervisor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>RENT</td>\n",
              "      <td>35000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>57.96</td>\n",
              "      <td>57000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>C3</td>\n",
              "      <td>Mar-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "   Unnamed: 0                emp_title  emp_length state homeownership  \\\n",
              "0           1  global config engineer          3.0    NJ      MORTGAGE   \n",
              "1           2   warehouse office clerk        10.0    HI          RENT   \n",
              "2           3                 assembly         3.0    WI          RENT   \n",
              "3           4         customer service         1.0    PA          RENT   \n",
              "4           5     security supervisor         10.0    CA          RENT   \n",
              "\n",
              "   annual_income  verified_income  debt_to_income  annual_income_joint  \\\n",
              "0        90000.0         Verified           18.01                  NaN   \n",
              "1        40000.0     Not Verified            5.04                  NaN   \n",
              "2        40000.0  Source Verified           21.15                  NaN   \n",
              "3        30000.0     Not Verified           10.16                  NaN   \n",
              "4        35000.0         Verified           57.96              57000.0   \n",
              "\n",
              "  verification_income_joint  ...  sub_grade  issue_month  loan_status  \\\n",
              "0                       NaN  ...         C3     Mar-2018      Current   \n",
              "1                       NaN  ...         C1     Feb-2018      Current   \n",
              "2                       NaN  ...         D1     Feb-2018      Current   \n",
              "3                       NaN  ...         A3     Jan-2018      Current   \n",
              "4                  Verified  ...         C3     Mar-2018      Current   \n",
              "\n",
              "   initial_listing_status  disbursement_method   balance  paid_total  \\\n",
              "0                   whole                 Cash  27015.86     1999.33   \n",
              "1                   whole                 Cash   4651.37      499.12   \n",
              "2              fractional                 Cash   1824.63      281.80   \n",
              "3                   whole                 Cash  18853.26     3312.89   \n",
              "4                   whole                 Cash  21430.15     2324.65   \n",
              "\n",
              "   paid_principal  paid_interest  paid_late_fees  \n",
              "0          984.14        1015.19             0.0  \n",
              "1          348.63         150.49             0.0  \n",
              "2          175.37         106.43             0.0  \n",
              "3         2746.74         566.15             0.0  \n",
              "4         1569.85         754.80             0.0  \n",
              "\n",
              "[5 rows x 56 columns]"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Preview the first rows\n",
        "df.head()"
      ]
    },
    {
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      "execution_count": null,
      "id": "b99428ed",
      "metadata": {
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        },
        "id": "b99428ed",
        "outputId": "3b5cdac5-8ac8-406d-d25e-3a95babde4d9"
      },
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              "      <td>45.26</td>\n",
              "      <td>107000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>E2</td>\n",
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              "      <td>Current</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>9999</td>\n",
              "      <td>manager</td>\n",
              "      <td>1.0</td>\n",
              "      <td>WI</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>80000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>11.99</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>A1</td>\n",
              "      <td>Feb-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>21518.12</td>\n",
              "      <td>2873.31</td>\n",
              "      <td>2481.88</td>\n",
              "      <td>391.43</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>10000</td>\n",
              "      <td>operations analyst</td>\n",
              "      <td>3.0</td>\n",
              "      <td>CT</td>\n",
              "      <td>RENT</td>\n",
              "      <td>66000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>20.82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>B4</td>\n",
              "      <td>Feb-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>11574.83</td>\n",
              "      <td>1658.56</td>\n",
              "      <td>1225.17</td>\n",
              "      <td>433.39</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 56 columns</p>\n",
              "</div>\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
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              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-2b47ccf3-abf3-4840-a5d0-ae013250439c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "      Unnamed: 0           emp_title  emp_length state homeownership  \\\n",
              "9995        9996              owner         10.0    TX          RENT   \n",
              "9996        9997            director         8.0    PA      MORTGAGE   \n",
              "9997        9998           toolmaker        10.0    CT      MORTGAGE   \n",
              "9998        9999             manager         1.0    WI      MORTGAGE   \n",
              "9999       10000  operations analyst         3.0    CT          RENT   \n",
              "\n",
              "      annual_income  verified_income  debt_to_income  annual_income_joint  \\\n",
              "9995       108000.0  Source Verified           22.28                  NaN   \n",
              "9996       121000.0         Verified           32.38                  NaN   \n",
              "9997        67000.0         Verified           45.26             107000.0   \n",
              "9998        80000.0  Source Verified           11.99                  NaN   \n",
              "9999        66000.0     Not Verified           20.82                  NaN   \n",
              "\n",
              "     verification_income_joint  ...  sub_grade  issue_month  loan_status  \\\n",
              "9995                       NaN  ...         A4     Jan-2018      Current   \n",
              "9996                       NaN  ...         D3     Feb-2018      Current   \n",
              "9997           Source Verified  ...         E2     Feb-2018      Current   \n",
              "9998                       NaN  ...         A1     Feb-2018      Current   \n",
              "9999                       NaN  ...         B4     Feb-2018      Current   \n",
              "\n",
              "      initial_listing_status  disbursement_method   balance  paid_total  \\\n",
              "9995                   whole                 Cash  21586.34     2969.80   \n",
              "9996                   whole                 Cash   9147.44     1456.31   \n",
              "9997              fractional                 Cash  27617.65     4620.80   \n",
              "9998                   whole                 Cash  21518.12     2873.31   \n",
              "9999                   whole                 Cash  11574.83     1658.56   \n",
              "\n",
              "      paid_principal  paid_interest  paid_late_fees  \n",
              "9995         2413.66         556.14             0.0  \n",
              "9996          852.56         603.75             0.0  \n",
              "9997         2382.35        2238.45             0.0  \n",
              "9998         2481.88         391.43             0.0  \n",
              "9999         1225.17         433.39             0.0  \n",
              "\n",
              "[5 rows x 56 columns]"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Preview the last rows\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5070fc22",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5070fc22",
        "outputId": "bc8649aa-fd9c-4824-f68e-abb0adcfa375"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Unnamed: 0',\n",
              " 'emp_title',\n",
              " 'emp_length',\n",
              " 'state',\n",
              " 'homeownership',\n",
              " 'annual_income',\n",
              " 'verified_income',\n",
              " 'debt_to_income',\n",
              " 'annual_income_joint',\n",
              " 'verification_income_joint',\n",
              " 'debt_to_income_joint',\n",
              " 'delinq_2y',\n",
              " 'months_since_last_delinq',\n",
              " 'earliest_credit_line',\n",
              " 'inquiries_last_12m',\n",
              " 'total_credit_lines',\n",
              " 'open_credit_lines',\n",
              " 'total_credit_limit',\n",
              " 'total_credit_utilized',\n",
              " 'num_collections_last_12m',\n",
              " 'num_historical_failed_to_pay',\n",
              " 'months_since_90d_late',\n",
              " 'current_accounts_delinq',\n",
              " 'total_collection_amount_ever',\n",
              " 'current_installment_accounts',\n",
              " 'accounts_opened_24m',\n",
              " 'months_since_last_credit_inquiry',\n",
              " 'num_satisfactory_accounts',\n",
              " 'num_accounts_120d_past_due',\n",
              " 'num_accounts_30d_past_due',\n",
              " 'num_active_debit_accounts',\n",
              " 'total_debit_limit',\n",
              " 'num_total_cc_accounts',\n",
              " 'num_open_cc_accounts',\n",
              " 'num_cc_carrying_balance',\n",
              " 'num_mort_accounts',\n",
              " 'account_never_delinq_percent',\n",
              " 'tax_liens',\n",
              " 'public_record_bankrupt',\n",
              " 'loan_purpose',\n",
              " 'application_type',\n",
              " 'loan_amount',\n",
              " 'term',\n",
              " 'interest_rate',\n",
              " 'installment',\n",
              " 'grade',\n",
              " 'sub_grade',\n",
              " 'issue_month',\n",
              " 'loan_status',\n",
              " 'initial_listing_status',\n",
              " 'disbursement_method',\n",
              " 'balance',\n",
              " 'paid_total',\n",
              " 'paid_principal',\n",
              " 'paid_interest',\n",
              " 'paid_late_fees']"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# List of all column names\n",
        "df.columns.tolist()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a38884ac",
      "metadata": {
        "id": "a38884ac"
      },
      "source": [
        "# Dataset Understanding\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ed16ac31",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ed16ac31",
        "outputId": "6f342041-7d36-4ec6-972e-e6bc88ea477f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 56 columns):\n",
            " #   Column                            Non-Null Count  Dtype  \n",
            "---  ------                            --------------  -----  \n",
            " 0   Unnamed: 0                        10000 non-null  int64  \n",
            " 1   emp_title                         9167 non-null   object \n",
            " 2   emp_length                        9183 non-null   float64\n",
            " 3   state                             10000 non-null  object \n",
            " 4   homeownership                     10000 non-null  object \n",
            " 5   annual_income                     10000 non-null  float64\n",
            " 6   verified_income                   10000 non-null  object \n",
            " 7   debt_to_income                    9976 non-null   float64\n",
            " 8   annual_income_joint               1495 non-null   float64\n",
            " 9   verification_income_joint         1455 non-null   object \n",
            " 10  debt_to_income_joint              1495 non-null   float64\n",
            " 11  delinq_2y                         10000 non-null  int64  \n",
            " 12  months_since_last_delinq          4342 non-null   float64\n",
            " 13  earliest_credit_line              10000 non-null  int64  \n",
            " 14  inquiries_last_12m                10000 non-null  int64  \n",
            " 15  total_credit_lines                10000 non-null  int64  \n",
            " 16  open_credit_lines                 10000 non-null  int64  \n",
            " 17  total_credit_limit                10000 non-null  int64  \n",
            " 18  total_credit_utilized             10000 non-null  int64  \n",
            " 19  num_collections_last_12m          10000 non-null  int64  \n",
            " 20  num_historical_failed_to_pay      10000 non-null  int64  \n",
            " 21  months_since_90d_late             2285 non-null   float64\n",
            " 22  current_accounts_delinq           10000 non-null  int64  \n",
            " 23  total_collection_amount_ever      10000 non-null  int64  \n",
            " 24  current_installment_accounts      10000 non-null  int64  \n",
            " 25  accounts_opened_24m               10000 non-null  int64  \n",
            " 26  months_since_last_credit_inquiry  8729 non-null   float64\n",
            " 27  num_satisfactory_accounts         10000 non-null  int64  \n",
            " 28  num_accounts_120d_past_due        9682 non-null   float64\n",
            " 29  num_accounts_30d_past_due         10000 non-null  int64  \n",
            " 30  num_active_debit_accounts         10000 non-null  int64  \n",
            " 31  total_debit_limit                 10000 non-null  int64  \n",
            " 32  num_total_cc_accounts             10000 non-null  int64  \n",
            " 33  num_open_cc_accounts              10000 non-null  int64  \n",
            " 34  num_cc_carrying_balance           10000 non-null  int64  \n",
            " 35  num_mort_accounts                 10000 non-null  int64  \n",
            " 36  account_never_delinq_percent      10000 non-null  float64\n",
            " 37  tax_liens                         10000 non-null  int64  \n",
            " 38  public_record_bankrupt            10000 non-null  int64  \n",
            " 39  loan_purpose                      10000 non-null  object \n",
            " 40  application_type                  10000 non-null  object \n",
            " 41  loan_amount                       10000 non-null  int64  \n",
            " 42  term                              10000 non-null  int64  \n",
            " 43  interest_rate                     10000 non-null  float64\n",
            " 44  installment                       10000 non-null  float64\n",
            " 45  grade                             10000 non-null  object \n",
            " 46  sub_grade                         10000 non-null  object \n",
            " 47  issue_month                       10000 non-null  object \n",
            " 48  loan_status                       10000 non-null  object \n",
            " 49  initial_listing_status            10000 non-null  object \n",
            " 50  disbursement_method               10000 non-null  object \n",
            " 51  balance                           10000 non-null  float64\n",
            " 52  paid_total                        10000 non-null  float64\n",
            " 53  paid_principal                    10000 non-null  float64\n",
            " 54  paid_interest                     10000 non-null  float64\n",
            " 55  paid_late_fees                    10000 non-null  float64\n",
            "dtypes: float64(17), int64(26), object(13)\n",
            "memory usage: 4.3+ MB\n"
          ]
        }
      ],
      "source": [
        "# Data types and non-null counts\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b8750451",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "b8750451",
        "outputId": "1fd83a8e-fecf-4bd6-f013-5b0a5c11d5c1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-6dd48f3b-9ad0-4a6b-8c92-acd3e0ea5bc7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>debt_to_income</th>\n",
              "      <th>annual_income_joint</th>\n",
              "      <th>debt_to_income_joint</th>\n",
              "      <th>delinq_2y</th>\n",
              "      <th>months_since_last_delinq</th>\n",
              "      <th>earliest_credit_line</th>\n",
              "      <th>inquiries_last_12m</th>\n",
              "      <th>...</th>\n",
              "      <th>public_record_bankrupt</th>\n",
              "      <th>loan_amount</th>\n",
              "      <th>term</th>\n",
              "      <th>interest_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>balance</th>\n",
              "      <th>paid_total</th>\n",
              "      <th>paid_principal</th>\n",
              "      <th>paid_interest</th>\n",
              "      <th>paid_late_fees</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.00000</td>\n",
              "      <td>9183.000000</td>\n",
              "      <td>1.000000e+04</td>\n",
              "      <td>9976.000000</td>\n",
              "      <td>1.495000e+03</td>\n",
              "      <td>1495.000000</td>\n",
              "      <td>10000.00000</td>\n",
              "      <td>4342.000000</td>\n",
              "      <td>10000.00000</td>\n",
              "      <td>10000.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5000.50000</td>\n",
              "      <td>5.930306</td>\n",
              "      <td>7.922215e+04</td>\n",
              "      <td>19.308192</td>\n",
              "      <td>1.279146e+05</td>\n",
              "      <td>19.979304</td>\n",
              "      <td>0.21600</td>\n",
              "      <td>36.760709</td>\n",
              "      <td>2001.29000</td>\n",
              "      <td>1.95820</td>\n",
              "      <td>...</td>\n",
              "      <td>0.123800</td>\n",
              "      <td>16361.922500</td>\n",
              "      <td>43.272000</td>\n",
              "      <td>12.427524</td>\n",
              "      <td>476.205323</td>\n",
              "      <td>14458.916610</td>\n",
              "      <td>2494.234773</td>\n",
              "      <td>1894.448466</td>\n",
              "      <td>599.666781</td>\n",
              "      <td>0.119516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2886.89568</td>\n",
              "      <td>3.703734</td>\n",
              "      <td>6.473429e+04</td>\n",
              "      <td>15.004851</td>\n",
              "      <td>7.016838e+04</td>\n",
              "      <td>8.054781</td>\n",
              "      <td>0.68366</td>\n",
              "      <td>21.634939</td>\n",
              "      <td>7.79551</td>\n",
              "      <td>2.38013</td>\n",
              "      <td>...</td>\n",
              "      <td>0.337172</td>\n",
              "      <td>10301.956759</td>\n",
              "      <td>11.029877</td>\n",
              "      <td>5.001105</td>\n",
              "      <td>294.851627</td>\n",
              "      <td>9964.561865</td>\n",
              "      <td>3958.230365</td>\n",
              "      <td>3884.407175</td>\n",
              "      <td>517.328062</td>\n",
              "      <td>1.813468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.920000e+04</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1963.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>5.310000</td>\n",
              "      <td>30.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2500.75000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.500000e+04</td>\n",
              "      <td>11.057500</td>\n",
              "      <td>8.683350e+04</td>\n",
              "      <td>14.160000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>1997.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8000.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>9.430000</td>\n",
              "      <td>256.040000</td>\n",
              "      <td>6679.065000</td>\n",
              "      <td>928.700000</td>\n",
              "      <td>587.100000</td>\n",
              "      <td>221.757500</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5000.50000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.500000e+04</td>\n",
              "      <td>17.570000</td>\n",
              "      <td>1.130000e+05</td>\n",
              "      <td>19.720000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>2003.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14500.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>11.980000</td>\n",
              "      <td>398.420000</td>\n",
              "      <td>12379.495000</td>\n",
              "      <td>1563.300000</td>\n",
              "      <td>984.990000</td>\n",
              "      <td>446.140000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7500.25000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.500000e+04</td>\n",
              "      <td>25.002500</td>\n",
              "      <td>1.515455e+05</td>\n",
              "      <td>25.500000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>2006.00000</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>24000.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>15.050000</td>\n",
              "      <td>644.690000</td>\n",
              "      <td>20690.182500</td>\n",
              "      <td>2616.005000</td>\n",
              "      <td>1694.555000</td>\n",
              "      <td>825.420000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10000.00000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2.300000e+06</td>\n",
              "      <td>469.090000</td>\n",
              "      <td>1.100000e+06</td>\n",
              "      <td>39.980000</td>\n",
              "      <td>13.00000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>2015.00000</td>\n",
              "      <td>29.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>40000.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>30.940000</td>\n",
              "      <td>1566.590000</td>\n",
              "      <td>40000.000000</td>\n",
              "      <td>41630.443684</td>\n",
              "      <td>40000.000000</td>\n",
              "      <td>4216.440000</td>\n",
              "      <td>52.980000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 43 columns</p>\n",
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              "            style=\"display:none;\">\n",
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              "\n",
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              "\n",
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              "      buttonEl.style.display =\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        Unnamed: 0   emp_length  annual_income  debt_to_income  \\\n",
              "count  10000.00000  9183.000000   1.000000e+04     9976.000000   \n",
              "mean    5000.50000     5.930306   7.922215e+04       19.308192   \n",
              "std     2886.89568     3.703734   6.473429e+04       15.004851   \n",
              "min        1.00000     0.000000   0.000000e+00        0.000000   \n",
              "25%     2500.75000     2.000000   4.500000e+04       11.057500   \n",
              "50%     5000.50000     6.000000   6.500000e+04       17.570000   \n",
              "75%     7500.25000    10.000000   9.500000e+04       25.002500   \n",
              "max    10000.00000    10.000000   2.300000e+06      469.090000   \n",
              "\n",
              "       annual_income_joint  debt_to_income_joint    delinq_2y  \\\n",
              "count         1.495000e+03           1495.000000  10000.00000   \n",
              "mean          1.279146e+05             19.979304      0.21600   \n",
              "std           7.016838e+04              8.054781      0.68366   \n",
              "min           1.920000e+04              0.320000      0.00000   \n",
              "25%           8.683350e+04             14.160000      0.00000   \n",
              "50%           1.130000e+05             19.720000      0.00000   \n",
              "75%           1.515455e+05             25.500000      0.00000   \n",
              "max           1.100000e+06             39.980000     13.00000   \n",
              "\n",
              "       months_since_last_delinq  earliest_credit_line  inquiries_last_12m  \\\n",
              "count               4342.000000           10000.00000         10000.00000   \n",
              "mean                  36.760709            2001.29000             1.95820   \n",
              "std                   21.634939               7.79551             2.38013   \n",
              "min                    1.000000            1963.00000             0.00000   \n",
              "25%                   19.000000            1997.00000             0.00000   \n",
              "50%                   34.000000            2003.00000             1.00000   \n",
              "75%                   53.000000            2006.00000             3.00000   \n",
              "max                  118.000000            2015.00000            29.00000   \n",
              "\n",
              "       ...  public_record_bankrupt   loan_amount          term  interest_rate  \\\n",
              "count  ...            10000.000000  10000.000000  10000.000000   10000.000000   \n",
              "mean   ...                0.123800  16361.922500     43.272000      12.427524   \n",
              "std    ...                0.337172  10301.956759     11.029877       5.001105   \n",
              "min    ...                0.000000   1000.000000     36.000000       5.310000   \n",
              "25%    ...                0.000000   8000.000000     36.000000       9.430000   \n",
              "50%    ...                0.000000  14500.000000     36.000000      11.980000   \n",
              "75%    ...                0.000000  24000.000000     60.000000      15.050000   \n",
              "max    ...                3.000000  40000.000000     60.000000      30.940000   \n",
              "\n",
              "        installment       balance    paid_total  paid_principal  \\\n",
              "count  10000.000000  10000.000000  10000.000000    10000.000000   \n",
              "mean     476.205323  14458.916610   2494.234773     1894.448466   \n",
              "std      294.851627   9964.561865   3958.230365     3884.407175   \n",
              "min       30.750000      0.000000      0.000000        0.000000   \n",
              "25%      256.040000   6679.065000    928.700000      587.100000   \n",
              "50%      398.420000  12379.495000   1563.300000      984.990000   \n",
              "75%      644.690000  20690.182500   2616.005000     1694.555000   \n",
              "max     1566.590000  40000.000000  41630.443684    40000.000000   \n",
              "\n",
              "       paid_interest  paid_late_fees  \n",
              "count   10000.000000    10000.000000  \n",
              "mean      599.666781        0.119516  \n",
              "std       517.328062        1.813468  \n",
              "min         0.000000        0.000000  \n",
              "25%       221.757500        0.000000  \n",
              "50%       446.140000        0.000000  \n",
              "75%       825.420000        0.000000  \n",
              "max      4216.440000       52.980000  \n",
              "\n",
              "[8 rows x 43 columns]"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Summary statistics for numerical features\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "44b51383",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "44b51383",
        "outputId": "4272370f-05ee-4f4c-959a-86a4cd02e78d"
      },
      "outputs": [
        {
          "data": {
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              "      <th>emp_title</th>\n",
              "      <th>state</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9167</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
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              "      <td>10000</td>\n",
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              "      <td>3</td>\n",
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              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>manager</td>\n",
              "      <td>CA</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>individual</td>\n",
              "      <td>B</td>\n",
              "      <td>B1</td>\n",
              "      <td>Mar-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2a8c4c2a-5217-4e0a-9b63-5fd567fe4921 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2a8c4c2a-5217-4e0a-9b63-5fd567fe4921');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       emp_title  state homeownership  verified_income  \\\n",
              "count       9167  10000         10000            10000   \n",
              "unique      4741     50             3                3   \n",
              "top      manager     CA      MORTGAGE  Source Verified   \n",
              "freq         218   1330          4789             4116   \n",
              "\n",
              "       verification_income_joint        loan_purpose application_type  grade  \\\n",
              "count                       1455               10000            10000  10000   \n",
              "unique                         3                  12                2      7   \n",
              "top                 Not Verified  debt_consolidation       individual      B   \n",
              "freq                         611                5144             8505   3037   \n",
              "\n",
              "       sub_grade issue_month loan_status initial_listing_status  \\\n",
              "count      10000       10000       10000                  10000   \n",
              "unique        32           3           6                      2   \n",
              "top           B1    Mar-2018     Current                  whole   \n",
              "freq         647        3617        9375                   8206   \n",
              "\n",
              "       disbursement_method  \n",
              "count                10000  \n",
              "unique                   2  \n",
              "top                   Cash  \n",
              "freq                  9284  "
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Summary statistics for categorical (object/string) features\n",
        "df.describe(include=\"object\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e908c379",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "e908c379",
        "outputId": "be6de428-427e-4f37-f49c-32f50d67c292"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_status</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Current</th>\n",
              "      <td>9375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fully Paid</th>\n",
              "      <td>447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>In Grace Period</th>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Late (31-120 days)</th>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Late (16-30 days)</th>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Charged Off</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ],
            "text/plain": [
              "loan_status\n",
              "Current               9375\n",
              "Fully Paid             447\n",
              "In Grace Period         67\n",
              "Late (31-120 days)      66\n",
              "Late (16-30 days)       38\n",
              "Charged Off              7\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Distribution of the loan_status column\n",
        "df[\"loan_status\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "97154fff",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "97154fff",
        "outputId": "f48107df-7820-48f6-f09a-0c180d4fd2a6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualise the raw loan_status distribution\n",
        "plt.figure(figsize=(9, 5))\n",
        "df[\"loan_status\"].value_counts().plot(kind=\"bar\", color=\"steelblue\")\n",
        "plt.title(\"Distribution of Loan Status (raw categories)\")\n",
        "plt.xlabel(\"Loan Status\")\n",
        "plt.ylabel(\"Number of Loans\")\n",
        "plt.xticks(rotation=45, ha=\"right\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "00cc0ce8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "00cc0ce8",
        "outputId": "a53c6a59-3325-4963-8268-9fbb0fb7a6cf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of numerical features: 43\n",
            "Number of categorical features: 13\n",
            "\n",
            "Categorical features: ['emp_title', 'state', 'homeownership', 'verified_income', 'verification_income_joint', 'loan_purpose', 'application_type', 'grade', 'sub_grade', 'issue_month', 'loan_status', 'initial_listing_status', 'disbursement_method']\n"
          ]
        }
      ],
      "source": [
        "# Separate numerical and categorical feature lists for later use\n",
        "numerical_features = df.select_dtypes(include=[\"int64\", \"float64\"]).columns.tolist()\n",
        "categorical_features = df.select_dtypes(include=[\"object\"]).columns.tolist()\n",
        "\n",
        "print(\"Number of numerical features:\", len(numerical_features))\n",
        "print(\"Number of categorical features:\", len(categorical_features))\n",
        "print()\n",
        "print(\"Categorical features:\", categorical_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5f0def2b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "5f0def2b",
        "outputId": "126e029b-616b-47fc-e32e-199d34198efa"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"missing_table[missing_table[\\\"missing_count\\\"] > 0]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"missing_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3862,\n        \"min\": 24,\n        \"max\": 8545,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          318,\n          8505,\n          833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"missing_percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.62810360058824,\n        \"min\": 0.24,\n        \"max\": 85.45,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          3.18,\n          85.05,\n          8.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-2a9b6ec5-12a8-4ca7-99aa-d817bada733a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>missing_count</th>\n",
              "      <th>missing_percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>verification_income_joint</th>\n",
              "      <td>8545</td>\n",
              "      <td>85.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annual_income_joint</th>\n",
              "      <td>8505</td>\n",
              "      <td>85.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>debt_to_income_joint</th>\n",
              "      <td>8505</td>\n",
              "      <td>85.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>months_since_90d_late</th>\n",
              "      <td>7715</td>\n",
              "      <td>77.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>months_since_last_delinq</th>\n",
              "      <td>5658</td>\n",
              "      <td>56.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>months_since_last_credit_inquiry</th>\n",
              "      <td>1271</td>\n",
              "      <td>12.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emp_title</th>\n",
              "      <td>833</td>\n",
              "      <td>8.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emp_length</th>\n",
              "      <td>817</td>\n",
              "      <td>8.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_accounts_120d_past_due</th>\n",
              "      <td>318</td>\n",
              "      <td>3.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>debt_to_income</th>\n",
              "      <td>24</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a9b6ec5-12a8-4ca7-99aa-d817bada733a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2a9b6ec5-12a8-4ca7-99aa-d817bada733a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2a9b6ec5-12a8-4ca7-99aa-d817bada733a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                  missing_count  missing_percent\n",
              "verification_income_joint                  8545            85.45\n",
              "annual_income_joint                        8505            85.05\n",
              "debt_to_income_joint                       8505            85.05\n",
              "months_since_90d_late                      7715            77.15\n",
              "months_since_last_delinq                   5658            56.58\n",
              "months_since_last_credit_inquiry           1271            12.71\n",
              "emp_title                                   833             8.33\n",
              "emp_length                                  817             8.17\n",
              "num_accounts_120d_past_due                  318             3.18\n",
              "debt_to_income                               24             0.24"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Missing values per column, sorted from highest to lowest\n",
        "missing_summary = df.isnull().sum().sort_values(ascending=False)\n",
        "missing_percent = (missing_summary / len(df)) * 100\n",
        "\n",
        "missing_table = pd.DataFrame({\n",
        "    \"missing_count\": missing_summary,\n",
        "    \"missing_percent\": missing_percent.round(2)\n",
        "})\n",
        "\n",
        "missing_table[missing_table[\"missing_count\"] > 0]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "63fdc3f5",
      "metadata": {
        "id": "63fdc3f5"
      },
      "source": [
        "# Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "45cd1405",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45cd1405",
        "outputId": "f941c9ad-d9e2-44ea-8326-a2860d35d307"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of duplicate rows found: 0\n",
            "Shape after removing duplicates: (10000, 56)\n"
          ]
        }
      ],
      "source": [
        "# Remove exact duplicate rows, if any\n",
        "duplicate_count = df.duplicated().sum()\n",
        "print(\"Number of duplicate rows found:\", duplicate_count)\n",
        "\n",
        "df = df.drop_duplicates()\n",
        "print(\"Shape after removing duplicates:\", df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "45ce40ee",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45ce40ee",
        "outputId": "5e9ea26e-d2b4-4eb4-d046-09e23caada1f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unnamed index column removed.\n"
          ]
        }
      ],
      "source": [
        "# Drop the unnamed index column carried over from the CSV export\n",
        "if \"Unnamed: 0\" in df.columns:\n",
        "    df = df.drop(columns=[\"Unnamed: 0\"])\n",
        "\n",
        "print(\"Unnamed index column removed.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eafc8ee8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "eafc8ee8",
        "outputId": "41002260-ab41-4e0f-a33b-16cf34b5b5a3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_joint_application</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1495</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ],
            "text/plain": [
              "is_joint_application\n",
              "0    8505\n",
              "1    1495\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create a flag for joint applications before dropping joint-only columns\n",
        "df[\"is_joint_application\"] = df[\"application_type\"].apply(\n",
        "    lambda x: 1 if str(x).lower() == \"joint\" else 0\n",
        ")\n",
        "\n",
        "df[\"is_joint_application\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6b16510d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6b16510d",
        "outputId": "cac17fd3-d7b5-4f2d-aa0b-667e3126c0be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape after dropping leakage and joint-only columns: (10000, 48)\n"
          ]
        }
      ],
      "source": [
        "# Drop columns containing post-loan repayment information and joint-only applicant features.\n",
        "leakage_columns = [\n",
        "    \"balance\",\n",
        "    \"paid_total\",\n",
        "    \"paid_principal\",\n",
        "    \"paid_interest\",\n",
        "    \"paid_late_fees\"\n",
        "]\n",
        "\n",
        "joint_only_columns = [\n",
        "    \"annual_income_joint\",\n",
        "    \"verification_income_joint\",\n",
        "    \"debt_to_income_joint\"\n",
        "]\n",
        "\n",
        "df = df.drop(columns=leakage_columns + joint_only_columns)\n",
        "\n",
        "print(\"Shape after dropping leakage and joint-only columns:\", df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "74d1c6f5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74d1c6f5",
        "outputId": "1a7a9be2-54b1-42ef-aaac-777616bb7866"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unique values in emp_title: 4741\n"
          ]
        }
      ],
      "source": [
        "# Remove the high-cardinality employment title feature from the dataset.\n",
        "print(\"Unique values in emp_title:\", df[\"emp_title\"].nunique())\n",
        "\n",
        "df = df.drop(columns=[\"emp_title\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f3efb9b6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3efb9b6",
        "outputId": "db64868c-55e8-4eea-868c-1a1f2cbdef45"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Remaining missing values:\n",
            "0\n"
          ]
        }
      ],
      "source": [
        "# Replace missing values using appropriate domain-specific assumptions.\n",
        "\n",
        "df[\"emp_length\"] = df[\"emp_length\"].fillna(0)\n",
        "\n",
        "df[\"months_since_last_delinq\"] = df[\"months_since_last_delinq\"].fillna(999)\n",
        "\n",
        "df[\"months_since_90d_late\"] = df[\"months_since_90d_late\"].fillna(999)\n",
        "\n",
        "df[\"months_since_last_credit_inquiry\"] = df[\"months_since_last_credit_inquiry\"].fillna(999)\n",
        "\n",
        "df[\"num_accounts_120d_past_due\"] = df[\"num_accounts_120d_past_due\"].fillna(0)\n",
        "\n",
        "df = df.dropna(subset=[\"debt_to_income\"])\n",
        "\n",
        "print(\"Remaining missing values:\")\n",
        "print(df.isnull().sum().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c6c874f3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "c6c874f3",
        "outputId": "7f923ab8-14af-484e-efe2-7ee1c15214ea"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1600x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Outlier inspection for key numeric fields using boxplots\n",
        "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n",
        "\n",
        "sns.boxplot(y=df[\"annual_income\"], ax=axes[0], color=\"lightblue\")\n",
        "axes[0].set_title(\"Annual Income\")\n",
        "\n",
        "sns.boxplot(y=df[\"debt_to_income\"], ax=axes[1], color=\"lightgreen\")\n",
        "axes[1].set_title(\"Debt to Income Ratio\")\n",
        "\n",
        "sns.boxplot(y=df[\"loan_amount\"], ax=axes[2], color=\"salmon\")\n",
        "axes[2].set_title(\"Loan Amount\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cdd70c46",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdd70c46",
        "outputId": "b2c62ca5-c1da-4121-8b0c-f609496e2b9c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Annual income capped at the 99th percentile: 300000.0\n"
          ]
        }
      ],
      "source": [
        "# Cap extreme annual income values at the 99th percentile.\n",
        "\n",
        "income_cap = df[\"annual_income\"].quantile(0.99)\n",
        "\n",
        "df[\"annual_income\"] = np.where(\n",
        "    df[\"annual_income\"] > income_cap,\n",
        "    income_cap,\n",
        "    df[\"annual_income\"]\n",
        ")\n",
        "\n",
        "print(\"Annual income capped at the 99th percentile:\", round(income_cap, 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "72bfea38",
      "metadata": {
        "id": "72bfea38"
      },
      "source": [
        "# Feature Engineering\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7460a323",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7460a323",
        "outputId": "5575f788-c8b1-4d1b-e12d-e9be64a903d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "loan_status\n",
            "Current               9354\n",
            "Fully Paid             445\n",
            "In Grace Period         66\n",
            "Late (31-120 days)      66\n",
            "Late (16-30 days)       38\n",
            "Charged Off              7\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Inspect the categories that will be grouped\n",
        "print(df[\"loan_status\"].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5bef1b1f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bef1b1f",
        "outputId": "cd809981-bc07-45bd-80f5-3b0ee3b65dd6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "default_risk\n",
            "0    9799\n",
            "1     177\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Positive class (default risk) proportion: 1.77 percent\n"
          ]
        }
      ],
      "source": [
        "# Define the binary target variable from loan_status\n",
        "default_categories = [\n",
        "    \"Charged Off\",\n",
        "    \"Late (31-120 days)\",\n",
        "    \"Late (16-30 days)\",\n",
        "    \"In Grace Period\"\n",
        "]\n",
        "\n",
        "df[\"default_risk\"] = df[\"loan_status\"].apply(\n",
        "    lambda x: 1 if x in default_categories else 0\n",
        ")\n",
        "\n",
        "print(df[\"default_risk\"].value_counts())\n",
        "print()\n",
        "print(\"Positive class (default risk) proportion:\",\n",
        "      round(df[\"default_risk\"].mean() * 100, 2), \"percent\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "155cb6df",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "155cb6df",
        "outputId": "1bf1dd10-3a86-4948-98ae-852fd1c4285a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualise the resulting class balance\n",
        "plt.figure(figsize=(6, 5))\n",
        "df[\"default_risk\"].value_counts().plot(kind=\"bar\", color=[\"seagreen\", \"indianred\"])\n",
        "plt.title(\"Target Variable Distribution: Default Risk\")\n",
        "plt.xlabel(\"Default Risk (0 = No, 1 = Yes)\")\n",
        "plt.ylabel(\"Number of Loans\")\n",
        "plt.xticks(rotation=0)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "889f4df9",
      "metadata": {
        "id": "889f4df9"
      },
      "outputs": [],
      "source": [
        "# Remove variables that may introduce target leakage into the prediction model.\n",
        "\n",
        "df = df.drop(columns=[\"loan_status\", \"grade\", \"sub_grade\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2bc36dc1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2bc36dc1",
        "outputId": "7f134c17-319f-4cfa-d34d-2b2ea23edfb0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape after encoding: (9976, 58)\n"
          ]
        }
      ],
      "source": [
        "# Encode categorical features using frequency, label, and one-hot encoding techniques.\n",
        "\n",
        "state_freq = df[\"state\"].value_counts(normalize=True)\n",
        "df[\"state_freq\"] = df[\"state\"].map(state_freq)\n",
        "df = df.drop(columns=[\"state\"])\n",
        "\n",
        "binary_like_columns = [\n",
        "    \"initial_listing_status\",\n",
        "    \"disbursement_method\",\n",
        "    \"application_type\"\n",
        "]\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "for column in binary_like_columns:\n",
        "    df[column] = label_encoder.fit_transform(df[column])\n",
        "\n",
        "low_cardinality_columns = [\n",
        "    \"homeownership\",\n",
        "    \"verified_income\",\n",
        "    \"loan_purpose\",\n",
        "    \"issue_month\"\n",
        "]\n",
        "\n",
        "df = pd.get_dummies(\n",
        "    df,\n",
        "    columns=low_cardinality_columns,\n",
        "    drop_first=True\n",
        ")\n",
        "\n",
        "print(\"Shape after encoding:\", df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ea626908",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "ea626908",
        "outputId": "2d3fb143-9916-4430-f8a6-614156e6af60"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>int64</th>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bool</th>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>float64</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ],
            "text/plain": [
              "int64      30\n",
              "bool       17\n",
              "float64    11\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Confirm no non-numeric columns remain before scaling\n",
        "df.dtypes.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f1f1a753",
      "metadata": {
        "id": "f1f1a753"
      },
      "source": [
        "# Exploratory Data Analysis (EDA)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f43c6dbc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "f43c6dbc",
        "outputId": "b7016311-d49b-46e5-ca92-34318eacbc2e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Loan amount distribution\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.histplot(df[\"loan_amount\"], bins=40, kde=True, color=\"steelblue\")\n",
        "plt.title(\"Distribution of Loan Amount\")\n",
        "plt.xlabel(\"Loan Amount\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "db263a76",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "db263a76",
        "outputId": "cb70cbf3-909b-4ee8-a9c0-fa1102f945f2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Annual income distribution\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.histplot(df[\"annual_income\"], bins=40, kde=True, color=\"seagreen\")\n",
        "plt.title(\"Distribution of Annual Income (capped at 99th percentile)\")\n",
        "plt.xlabel(\"Annual Income\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "49eb5040",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "49eb5040",
        "outputId": "8c6f8a69-e7c7-4454-9bbc-173d5ebf1b2f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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NwQKAJOnMmTNKTEzMdHBxvWLFimnp0qXasWOHNm3apC1btujzzz/XRx99pPfffz9Hj4jMzX0RZlmt1jv22MrsXierA6WCJrvaC9KY2rVrp8jISK1bt04DBw7U2rVr5evrq/vuuy9T3+xCbnbtGeNJT0+Xi4uL5s+fn+XYb3wakCMeiZrxqfvjjz+ujh07Ztnnxgcu3Grc+Smv28TT09N2D0xW62zfvr0+/vhjjRkzRj///LPOnTtnd29NxliGDx+e7WNwM35/48ePt917ERgYqFKlSsnFxUWvvPJKltvknnvuydT26KOPqlGjRvr666/1ww8/aMGCBZo/f75mz56d6V6SS5cuZQregLMiWACQ9M+TcCQpNDT0pv1cXV0VHBys4OBgvf7664qKitKMGTO0Y8cOhYSE5Psnczc+vcYwDB07dszu4Cm7MwOnT5+2+wQxN7VVqVJFMTExSkpKsjtrcfToUdv8/FClShXFxsYqPT3d7gAw43Vu/LS5oDp27Jgeeugh2/Tly5cVFxenhx9+ONtlcjP2MmXKqGnTplq3bp3at2+vn3/+OdMN9GZVr15dhmGoatWqqlmzZr6sM7//HsqVK6cSJUooPT09R2ejciJjOx8/ftzud5iWlqZTp07Z/a3drk/eixQpourVq2f7pLMOHTro/fff17fffqvNmzerXLlydu9VGX/nJUuWvOV22bBhg5544gm7J8ldu3Yty7MVN1OhQgV1795d3bt3V0JCgjp27KioqCi7YJGWlqa//vor0xPnAGfFPRYAFBMTo3fffVdVq1bN9ISd6124cCFTW8angxmP7cx4osqtLgHKqTVr1th9K/iXX36Z6YC1WrVq+uWXX+weHfrdd99lekRobmp7+OGHZbVabY9dzfDBBx/IxcXlpgfMufHwww8rLi5On3/+ua0tLS1NixcvVvHixfXAAw/ky+vcbh999JFSU1Nt08uXL1daWtpNt1Nux96hQwcdOXJEU6ZMkZubW75/O3OrVq3k5uamd955J9Mn14Zh5Ok6+fz+e3Bzc1Pr1q21YcMG/fbbb5nmZ/UUsFvx9fVVmTJl9PHHH9vuN5H+uY/hxkcG5/d4rhcYGKj9+/dnOa9OnTry8fHRypUr9dVXX6ldu3Z2l6b5+vqqevXqev/997O8T+z67ZLVWZXFixfLarXmqE6r1ZophJQvX14VKlTI9PjiI0eO6Nq1awoKCsrRuoHCjjMWwF1m8+bNOnr0qKxWq+Lj47Vjxw798MMPqly5subOnZvlaf8Mc+bM0U8//aTw8HBVqVJFCQkJWrZsmSpVqmR79GTGTZArVqxQiRIlVLx4cfn7+2d57XFOeHp66plnnlGnTp1sj5utUaOG3SNxu3Tpog0bNuj5559X27Ztdfz4ca1bty7TZV25qa158+Zq3LixZsyYYfvU9ocfftA333yj3r173/SSsdzo1q2bPvroI0VEROjAgQOqUqWKNmzYYPtE/sZ7PHLj7NmztjNR1ytRooQeeeQRM2Vnkpqaqj59+qht27b6448/tGzZMjVs2FAtWrTIdpncjj08PFxlypTRl19+qYcffviWjyrOrerVq+vll1/WtGnTdOrUKT3yyCMqUaKETp48qY0bN6pr167q27dvrteZl7+HVatWacuWLZnae/Xqpddee007duxQ165d1aVLF9WuXVsXL17UgQMHFBMTo507d+aqRnd3d7300ksaP368evfurbZt2+rUqVOKjo429TeUWy1atNCnn36qP/74I8szRk888YQmT54sSZk+AHF1ddWECRPUr18/PfbYY+rUqZMqVqyos2fPaseOHSpZsqSioqIkSU2bNtWnn36qkiVLqnbt2tqzZ4+2bdtm9yjjm7l8+bLCw8PVunVr1alTR8WLF9e2bdu0b9++TN+ns23bNnl4eOTb2SWgoCNYAHeZWbNmSfrn6TtlypSRxWLRyJEj1alTp1sexDZv3lynTp3SqlWr9Pfff6ts2bJ68MEH9dJLL9lukC1atKgmTZqk6dOna8yYMUpLS1NkZGSeDzwGDhyo2NhY/e9//9Ply5cVHBys0aNH2z1rPiwsTBEREVq4cKEmTpwoX19fRUVF2Q5CMuSmNldXV82dO1ezZs3S559/bvvOg+HDh+u5557L01iyUqxYMS1evFhTp07V6tWrlZSUpJo1ayoyMjJXT8/KysGDBzV8+PBM7VWqVMn3YPHmm29q3bp1mjVrllJTU9WuXTuNGjXqppfO5Hbs7u7uevTRR7Vs2bLb9o3i/fv317/+9S998MEHmjNnjqR/bkhu0qRJni5nyevfw/Lly7Ns79SpkypVqqRPPvlEc+bM0ddff63ly5erTJkyql27toYNG5brGiWpR48eMgxDCxcu1OTJk1WnTh3NnTtXEyZMsPuwIb//vq/XrFkzlS1bVl988YUGDRqUaX779u01depUVatWzfadFNdr3LixPvroI7377rtasmSJkpOT5e3tLX9/f3Xr1s3W74033pCrq6vWrVuna9euqUGDBlq4cKGef/75HNVZrFgxPf300/rhhx/01VdfyTAMVa9eXaNHj9Yzzzxj1/fLL79Uy5YtTX1AABQmLkZhuLMQAFAgRUdH6/XXX9fKlSvl5+d3219v4sSJWrlypX744YdMX2SG/JWenm578tuECRPuyGvOmTNH0dHR+uqrrzJdsnT+/HmFhYVp0KBBevHFF+9IPWYcPHhQHTt21OrVq7O9oRxwNtxjAQAoFK5du6a1a9eqdevWhIp8du3atUz3laxZs0YXLlzQgw8+eMfq6NOnj5KTk7V+/fpM81avXi2r1Xrbzlblt//9739q3bo1oQJ3FS6FAgAUaAkJCdq2bZs2bNigCxcuqFevXo4uyens2bNHkZGRatOmjcqUKaNff/1VK1eulMViUZs2be5YHSVKlFBMTIxdW0xMjH7//XdFRUXpkUceyfJ7JQqiGTNmOLoE4I4jWAAACrQjR45o2LBhKl++vEaNGsUnwLdBlSpVVKlSJS1evFgXL16Up6enOnTooGHDhuXqCy1vh3fffVe7d+9WUFCQ/vOf/zi0FgA3xz0WAAAAAEzjHgsAAAAAphEsAAAAAJjGPRY5kJ6errS0NLm6ut70mewAAACAMzEMQ+np6SpSpIhcXW9+ToJgkQNpaWnat2+fo8sAAAAAHMLPz++WD3MgWORARjrz8/PL9IU9AAAAgLOyWq3at2/fLc9WSASLHMm4/MnNzY1gAQAAgLtOTm4H4OZtAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAgDxITzccXUK+MjueIvlUBwAAAHBXcXV10bL3f9W5M8mOLsW0CpWK65nn6plaB8ECAAAAyKNzZ5J16kSSo8soELgUCgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmOTRYLFu2TO3bt1eDBg3UoEEDdevWTd9//71t/rVr1zR27Fg1btxYQUFBeumllxQfH2+3jtOnT6t///4KCAhQcHCwJk+erLS0NLs+O3bsUMeOHeXr66uWLVsqOjr6jowPAAAAuFs4NFhUqlRJw4YNU3R0tFatWqWHHnpIL774og4fPixJmjhxor777jvNnDlTixcv1rlz5zR48GDb8larVQMGDFBqaqpWrFihSZMmafXq1Zo1a5atz4kTJzRgwAA1btxYn376qXr37q1Ro0Zpy5Ytd3y8AAAAgLNyaLBo3ry5wsPD9a9//Us1a9bUK6+8ouLFi2vPnj1KTEzUqlWrFBERoeDgYPn6+mrixInavXu39uzZI0naunWrjhw5orffflt169ZVeHi4hg4dqqVLlyolJUWStGLFClWtWlURERGqVauWevToodatW+uDDz5w3MABAAAAJ1Ng7rGwWq1av369kpOTFRQUpP379ys1NVUhISG2PrVq1VLlypVtwWLPnj2yWCzy8vKy9QkNDVVSUpKOHDli6xMcHGz3WqGhobZ1AAAAADCviKMLiI2N1VNPPaVr166pePHimjNnjmrXrq2DBw+qaNGiKl26tF3/8uXLKy4uTpIUHx9vFyok2aZv1ScpKUlXr15VsWLFclyr1WrN9fgAAADgnNzc3BxdQr678Xg3N8e/Dg8WNWvW1Jo1a5SYmKgNGzZoxIgRWrJkiaPLytK+ffscXQIAAAAKAA8PD9WrV8/RZeS72NhYXblyJU/LOjxYuLu7q0aNGpIkX19f7du3T4sWLVLbtm2VmpqqS5cu2Z21SEhIkLe3t6R/zjzs3bvXbn0ZT426vs+NT5KKj49XyZIlc3W2QpL8/PycMpkCAAAAkuTj42M3bbVac/zhusODxY3S09OVkpIiX19fFS1aVDExMWrdurUk6ejRozp9+rQCAwMlSYGBgYqKilJCQoLKly8vSdq2bZtKliyp2rVr2/ps3rzZ7jW2bdtmW0duuLm5ESwAAADgtMwc6zr05u1p06bpxx9/1MmTJxUbG6tp06Zp586dat++vUqVKqXOnTtr0qRJ2r59u/bv36+RI0cqKCjIFgpCQ0NVu3ZtDR8+XIcOHdKWLVs0c+ZMde/eXe7u7pKkp556SidOnNCUKVP0+++/a+nSpfriiy/Up08fxw0cAAAAcDIOPWORkJCgESNG6Ny5cypVqpR8fHy0YMECNWnSRJI0cuRIubq6asiQIUpJSVFoaKhGjx5tW97NzU1RUVEaM2aMunXrJg8PD3Xs2FFDhgyx9alWrZrmzZunyMhILVq0SJUqVdKECRMUFhZ2x8cLAAAAOCsXwzAMRxdR0FmtVu3Zs0eBgYFcCgUAAACbmRN/0qkTSY4uw7Qq1Urq5ZGNMrXn5ji4wHyPBQAAAIDCi2ABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0xwaLObNm6fOnTsrKChIwcHBGjRokI4ePWrXp2fPnvLx8bH7efPNN+36nD59Wv3791dAQICCg4M1efJkpaWl2fXZsWOHOnbsKF9fX7Vs2VLR0dG3fXwAAADA3aKII198586d6t69u/z8/GS1WjV9+nT17dtX69evV/HixW39unbtqiFDhtimPTw8bP+2Wq0aMGCAvLy8tGLFCp07d04jRoxQ0aJF9eqrr0qSTpw4oQEDBuipp57S1KlTFRMTo1GjRsnb21thYWF3bsAAAACAk3JosFiwYIHd9KRJkxQcHKwDBw7ogQcesLUXK1ZM3t7eWa5j69atOnLkiBYuXCgvLy/VrVtXQ4cO1dSpUzV48GC5u7trxYoVqlq1qiIiIiRJtWrV0q5du/TBBx8QLAAAAIB8UKDusUhMTJQkeXp62rWvW7dOjRs31mOPPaZp06bpypUrtnl79uyRxWKRl5eXrS00NFRJSUk6cuSIrU9wcLDdOkNDQ7Vnz57bNBIAAADg7uLQMxbXS09P18SJE9WgQQNZLBZb+2OPPabKlSurQoUKio2N1dSpU/XHH3/onXfekSTFx8fbhQpJtum4uLib9klKStLVq1dVrFixHNVotVrzPD4AAAA4Fzc3N0eXkO9uPN7NzfFvgQkWY8eO1eHDh7Vs2TK79m7dutn+7ePjI29vb/Xp00fHjx9X9erV72iN+/btu6OvBwAAgILJw8ND9erVc3QZ+S42Ntbu6qDcKBDBYty4cdq0aZOWLFmiSpUq3bRvQECAJOnYsWOqXr26vLy8tHfvXrs+8fHxkmS7L8PLy8vWdn2fkiVL5vhshST5+fk5ZTIFAAAApH8+yL+e1WrN8YfrDg0WhmFo/Pjx+vrrr7V48WJVq1btlsscPHhQ0v+FhsDAQEVFRSkhIUHly5eXJG3btk0lS5ZU7dq1bX02b95st55t27YpMDAwV/W6ubkRLAAAAOC0zBzrOvTm7bFjx2rt2rWaNm2aSpQoobi4OMXFxenq1auSpOPHj2vOnDnav3+/Tp48qW+++UYjRozQAw88oDp16kj65ybs2rVra/jw4Tp06JC2bNmimTNnqnv37nJ3d5ckPfXUUzpx4oSmTJmi33//XUuXLtUXX3yhPn36OGroAAAAgFNx6BmL5cuXS/rnS/CuFxkZqU6dOqlo0aKKiYnRokWLlJycrHvvvVetWrXSoEGDbH3d3NwUFRWlMWPGqFu3bvLw8FDHjh3tvveiWrVqmjdvniIjI7Vo0SJVqlRJEyZM4FGzAAAAQD5xaLCIjY296fx7771XS5YsueV6qlSpovnz59+0T+PGjbVmzZrclAcAAAAghwrU91gAAAAAKJwIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADDNocFi3rx56ty5s4KCghQcHKxBgwbp6NGjdn2uXbumsWPHqnHjxgoKCtJLL72k+Ph4uz6nT59W//79FRAQoODgYE2ePFlpaWl2fXbs2KGOHTvK19dXLVu2VHR09G0fHwAAAHC3cGiw2Llzp7p3766PP/5YCxcuVFpamvr27avk5GRbn4kTJ+q7777TzJkztXjxYp07d06DBw+2zbdarRowYIBSU1O1YsUKTZo0SatXr9asWbNsfU6cOKEBAwaocePG+vTTT9W7d2+NGjVKW7ZsuaPjBQAAAJxVEUe++IIFC+ymJ02apODgYB04cEAPPPCAEhMTtWrVKk2dOlXBwcGS/gkajz76qPbs2aPAwEBt3bpVR44c0cKFC+Xl5aW6detq6NChmjp1qgYPHix3d3etWLFCVatWVUREhCSpVq1a2rVrlz744AOFhYXd8XEDAAAAzqZA3WORmJgoSfL09JQk7d+/X6mpqQoJCbH1qVWrlipXrqw9e/ZIkvbs2SOLxSIvLy9bn9DQUCUlJenIkSO2PhnB5Po+GesAAAAAYI5Dz1hcLz09XRMnTlSDBg1ksVgkSfHx8SpatKhKly5t17d8+fKKi4uz9bk+VEiyTd+qT1JSkq5evapixYrlqEar1Zr7gQEAAMApubm5ObqEfHfj8W5ujn8LTLAYO3asDh8+rGXLljm6lGzt27fP0SUAAACgAPDw8FC9evUcXUa+i42N1ZUrV/K0bIEIFuPGjdOmTZu0ZMkSVapUydbu5eWl1NRUXbp0ye6sRUJCgry9vW199u7da7e+jKdGXd/nxidJxcfHq2TJkjk+WyFJfn5+TplMAQAAAEny8fGxm7ZarTn+cN2hwcIwDI0fP15ff/21Fi9erGrVqtnN9/X1VdGiRRUTE6PWrVtLko4eParTp08rMDBQkhQYGKioqCglJCSofPnykqRt27apZMmSql27tq3P5s2b7da9bds22zpyys3NjWABAAAAp2XmWNehN2+PHTtWa9eu1bRp01SiRAnFxcUpLi5OV69elSSVKlVKnTt31qRJk7R9+3bt379fI0eOVFBQkC0UhIaGqnbt2ho+fLgOHTqkLVu2aObMmerevbvc3d0lSU899ZROnDihKVOm6Pfff9fSpUv1xRdfqE+fPg4aOQAAAOBcHHrGYvny5ZKknj172rVHRkaqU6dOkqSRI0fK1dVVQ4YMUUpKikJDQzV69GhbXzc3N0VFRWnMmDHq1q2bPDw81LFjRw0ZMsTWp1q1apo3b54iIyO1aNEiVapUSRMmTOBRswAAAEA+cTEMw3B0EQWd1Wq1fW8Gl0IBAAAgw8yJP+nUiSRHl2FalWol9fLIRpnac3McXKC+xwIAAABA4USwAAAAAGAawQIAAACAaQQLAAAAAKblKVi0aNFCf//9d6b2S5cuqUWLFqaLAgAAAFC45ClYnDp1Sunp6ZnaU1JSdPbsWdNFAQAAAChccvU9Ft98843t31u2bFGpUqVs0+np6YqJiVGVKlXyrzoAAAAAhUKugsWLL74oSXJxcVFERIT9iooUUZUqVTK1AwAAAHB+uQoWhw4dkiQ1b95cK1euVLly5W5LUQAAAAAKl1wFiwzffvttftcBAAAAoBDLU7CQpJiYGMXExCghISHTjdyRkZGmCwMAAABQeOQpWLzzzjuaM2eOfH195e3tLRcXl/yuCwAAAEAhkqdgsWLFCkVGRuqJJ57I53IAAAAAFEZ5+h6L1NRUNWjQIL9rAQAAAFBI5SlYPPnkk1q3bl1+1wIAAACgkMrTpVDXrl3Txx9/rJiYGPn4+KhIEfvVvP766/lSHAAAAIDCIU/BIjY2VnXq1JEk/fbbb3bzuJEbAAAAuPvkKVgsXrw4v+sAAAAAUIjl6R4LAAAAALhens5Y9OzZ86aXPC1atCjPBQEAAAAofPIULOrWrWs3nZaWpoMHD+rw4cN8twUAAABwF8pTsBg5cmSW7bNnz1ZycrKpggAAAAAUPvl6j8Xjjz+uVatW5ecqAQAAABQC+Rosdu/eLXd39/xcJQAAAIBCIE+XQg0ePNhu2jAMxcXFaf/+/Ro0aFC+FAYAAACg8MhTsChVqpTdtIuLi2rWrKkhQ4YoNDQ0XwoDAAAAUHjkKVhERkbmdx0AAAAACrE8BYsM+/fv1++//y5Juv/++1WvXr18KQoAAABA4ZKnYJGQkKBXXnlFO3fuVOnSpSVJly5dUuPGjTVjxgyVK1cuX4sEAAAAULDl6alQ48eP1+XLl7V+/Xrt3LlTO3fu1GeffaakpCRNmDAhv2sEAAAAUMDlKVhs2bJFo0ePVq1atWxttWvX1ujRo7V58+Z8Kw4AAABA4ZCnYJGenq6iRYtmai9SpIjS09NNFwUAAACgcMlTsHjooYf01ltv6ezZs7a2s2fPKjIyUsHBwflWHAAAAIDCIU83b7/55pt64YUX1KJFC1WqVEmSdObMGd1///16++2387VAAAAAAAVfnoLFvffeq9WrV2vbtm06evSoJKlWrVoKCQnJ1+IAAAAAFA65uhQqJiZGjz76qJKSkuTi4qImTZqoZ8+e6tmzp/z8/NSuXTv99NNPt6tWAAAAAAVUroLFhx9+qK5du6pkyZKZ5pUqVUrdunXTwoUL8604AAAAAIVDroJFbGyswsLCsp3fpEkTHThwwHRRAAAAAAqXXAWL+Ph4FSmS/W0ZRYoU0fnz500XBQAAAKBwyVWwqFixog4fPpzt/NjYWHl7e5suCgAAAEDhkqtgER4erv/+97+6du1apnlXr17V7Nmz1axZs3wrDgAAAEDhkKvHzb7wwgv66quv1Lp1a3Xv3l01a9aUJB09elTLli2T1WrVwIEDb0uhAAAAAAquXAULLy8vrVixQmPGjNH06dNlGIYkycXFRaGhoXrzzTfl5eV1WwoFAAAAUHDl+gvyqlSpovnz5+vixYs6duyYJKlGjRry9PTM9+IAAAAAFA55+uZtSfL09JS/v39+1gIAAACgkMrVzdsAAAAAkBWCBQAAAADTHBosfvzxRw0cOFChoaHy8fHRxo0b7eZHRETIx8fH7qdv3752fS5cuKDXXntNDRo0UKNGjTRy5EhdvnzZrs+hQ4f0zDPPyM/PT+Hh4Zo/f/5tHxsAAABwN8nzPRb5ITk5WT4+PurcubMGDx6cZZ+wsDBFRkbapt3d3e3mDxs2THFxcVq4cKFSU1M1cuRIvfnmm5o2bZokKSkpSX379lVwcLDGjh2r3377TSNHjlTp0qXVrVu32zc4AAAA4C7i0GARHh6u8PDwm/Zxd3fP9tu8f//9d23ZskUrV66Un5+fJGnUqFHq37+/hg8frooVK2rt2rVKTU3VxIkT5e7urvvvv18HDx7UwoULCRYAAABAPnFosMiJnTt3Kjg4WKVLl9ZDDz2kl19+WWXLlpUk7d69W6VLl7aFCkkKCQmRq6ur9u7dq5YtW2rPnj1q1KiR3ZmO0NBQ2yNzc/OYXKvVmn8DAwAAQKHm5ubm6BLy3Y3Hu7k5/i3QwSIsLEwtW7ZU1apVdeLECU2fPl39+vXTRx99JDc3N8XHx6tcuXJ2yxQpUkSenp6Ki4uTJMXHx6tq1ap2fTK+xC8+Pj5XwWLfvn0mRwQAAABn4OHhoXr16jm6jHwXGxurK1eu5GnZAh0s2rVrZ/t3xs3bjzzyiO0sxp3m5+fnlMkUAAAAkP455r6e1WrN8YfrBTpY3KhatWoqW7asjh07puDgYHl5een8+fN2fdLS0nTx4kXbfRleXl6Kj4+365MxnXHmIqfc3NyyDBbp6YZcXV1yta6CypnGAgAAgNwx8yF6oQoWZ86c0YULF2yhISgoSJcuXdL+/fvl6+srSdq+fbvS09Nt3woeGBiomTNnKjU1VUWLFpUkbdu2TTVr1szVZVA34+rqomXv/6pzZ5LzZX2OUqFScT3znPOd0gMAAMDt59BgcfnyZR0/ftw2ffLkSR08eFCenp7y9PTUO++8o9atW8vLy0snTpzQ22+/rRo1aigsLEySVKtWLYWFhek///mPxo4dq9TUVI0fP17t2rVTxYoVJUnt27fXnDlz9MYbb6hfv346fPiwFi1apNdffz1fx3LuTLJOnUjK13UCAAAAhYVDg8X+/fvVq1cv23TG91V07NhRY8aM0W+//aY1a9YoMTFRFSpUUJMmTTR06FC7JzxNnTpV48ePV+/eveXq6qpWrVpp1KhRtvmlSpXSggULNG7cOHXq1Elly5bVoEGDeNQsAAAAkI8cGiwaN26s2NjYbOcvWLDglusoU6aM7cvwslOnTh0tW7Ys1/UBAAAAyBlXRxcAAAAAoPAjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0hwaLH3/8UQMHDlRoaKh8fHy0ceNGu/mGYei///2vQkND5e/vrz59+ujPP/+063PhwgW99tpratCggRo1aqSRI0fq8uXLdn0OHTqkZ555Rn5+fgoPD9f8+fNv99AAAACAu4pDg0VycrJ8fHw0evToLOfPnz9fixcv1pgxY/Txxx/Lw8NDffv21bVr12x9hg0bpiNHjmjhwoWKiorSTz/9pDfffNM2PykpSX379lXlypUVHR2t4cOH65133tFHH31028cHAAAA3C2KOPLFw8PDFR4enuU8wzC0aNEivfDCC3rkkUckSVOmTFFISIg2btyodu3a6ffff9eWLVu0cuVK+fn5SZJGjRql/v37a/jw4apYsaLWrl2r1NRUTZw4Ue7u7rr//vt18OBBLVy4UN26dbtjYwUAAACcmUODxc2cPHlScXFxCgkJsbWVKlVKAQEB2r17t9q1a6fdu3erdOnStlAhSSEhIXJ1ddXevXvVsmVL7dmzR40aNZK7u7utT2hoqObPn6+LFy/K09MzxzVZrdYs293c3PIwwoIru3ECAADg/zjbMaCU+TgwN8eFBTZYxMXFSZLKly9v116+fHnFx8dLkuLj41WuXDm7+UWKFJGnp6dt+fj4eFWtWtWuj5eXl21eboLFvn37MrV5eHioXr16OV5HYRAbG6srV644ugwAAIACyxmPASVzx4EFNlgURH5+fk6ZTG/k4+Pj6BIAAADgADceB1qt1iw/XM9KgQ0W3t7ekqSEhARVqFDB1p6QkKA6depI+ufMw/nz5+2WS0tL08WLF23Le3l52c5wZMiYzjhzkVNubm53RbC4G8YIAACAzMwcBxbY77GoWrWqvL29FRMTY2tLSkrSL7/8oqCgIElSUFCQLl26pP3799v6bN++Xenp6fL395ckBQYG6qefflJqaqqtz7Zt21SzZs1cXQYFAAAAIHsODRaXL1/WwYMHdfDgQUn/3LB98OBBnT59Wi4uLurVq5fmzp2rb775RrGxsRo+fLgqVKhge0pUrVq1FBYWpv/85z/au3evdu3apfHjx6tdu3aqWLGiJKl9+/YqWrSo3njjDR0+fFiff/65Fi1apGeffdZh44ZzSU83HF1CvnGmsQAAgDvLoZdC7d+/X7169bJNR0ZGSpI6duyoSZMmqV+/frpy5YrefPNNXbp0SQ0bNtR7772ne+65x7bM1KlTNX78ePXu3Vuurq5q1aqVRo0aZZtfqlQpLViwQOPGjVOnTp1UtmxZDRo0iEfNIt+4urpo2fu/6tyZZEeXYkqFSsX1zHPOdxMaAAC4MxwaLBo3bqzY2Nhs57u4uGjo0KEaOnRotn3KlCmjadOm3fR16tSpo2XLluW5TuBWzp1J1qkTSY4uAwAAwGEK7D0WAAAAAAoPggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAFPS0w1Hl5CvnG08AADcKUUcXQCAws3V1UXL3v9V584kO7oU0ypUKq5nnqvn6DIAACiUCBYATDt3JlmnTiQ5ugwAAOBAXAoFAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFjAtPd1wdAn5xpnGAgAAcCcVcXQBKPxcXV207P1fde5MsqNLMaVCpeJ65rl6ji4DAACgUCJYIF+cO5OsUyeSHF0GAAAAHIRLoQAAAACYRrAAAJjiTPcmOdNYAOBO41IoAIAp3GcFAJAIFgCAfMB9VgAALoUCAAAAYBrBAgBMcLZr8p1tPACAO4dLoQDABGe5v0DiHgMAgDkECwAwifsLAADgUigAAAAA+YBgAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwrUAHi9mzZ8vHx8fup02bNrb5165d09ixY9W4cWMFBQXppZdeUnx8vN06Tp8+rf79+ysgIEDBwcGaPHmy0tLS7vRQAAAAAKdW4B83e//992vhwoW2aTc3N9u/J06cqO+//14zZ85UqVKlNH78eA0ePFgrVqyQJFmtVg0YMEBeXl5asWKFzp07pxEjRqho0aJ69dVX7/hYAAAAAGdVoM9YSP8ECW9vb9tPuXLlJEmJiYlatWqVIiIiFBwcLF9fX02cOFG7d+/Wnj17JElbt27VkSNH9Pbbb6tu3boKDw/X0KFDtXTpUqWkpDhwVAAAAIBzKfDB4tixYwoNDVWLFi302muv6fTp05Kk/fv3KzU1VSEhIba+tWrVUuXKlW3BYs+ePbJYLPLy8rL1CQ0NVVJSko4cOXJHxwEAAAA4swJ9KZS/v78iIyNVs2ZNxcXFac6cOerevbvWrVun+Ph4FS1aVKVLl7Zbpnz58oqLi5MkxcfH24UKSbbpjD65YbVas2y//vIsZ5DdOLPD+Bm/s8nNNrjbxy853zbI7fgB3L2c7f1PyvwemJv3xAIdLMLDw23/rlOnjgICAtSsWTN98cUXKlas2B2vZ9++fZnaPDw8VK9evTtey+0UGxurK1eu5Kgv42f8zjZ+Kefb4G4fv+Sc2yA34wdw93LG9z/J3HtggQ4WNypdurT+9a9/6fjx4woJCVFqaqouXbpkd9YiISFB3t7ekv45O7F37167dWQ8NSqjT274+fk5ZTK9kY+Pj6NLcCjGf3ePX2IbMP67e/wA7m43vgdardYsP1zPSqEKFpcvX9aJEyfk7e0tX19fFS1aVDExMWrdurUk6ejRozp9+rQCAwMlSYGBgYqKilJCQoLKly8vSdq2bZtKliyp2rVr5/r13dzc7opgcTeM8WYY/909foltwPjv7vEDuLuZeQ8s0MFi8uTJatasmSpXrqxz585p9uzZcnV11WOPPaZSpUqpc+fOmjRpkjw9PVWyZElNmDBBQUFBtmARGhqq2rVra/jw4fr3v/+tuLg4zZw5U927d5e7u7tjBwcAAAA4kQIdLM6cOaNXX31VFy5cULly5dSwYUN9/PHHtkfOjhw5Uq6urhoyZIhSUlIUGhqq0aNH25Z3c3NTVFSUxowZo27dusnDw0MdO3bUkCFDHDUkAAAAwCkV6GAxY8aMm86/5557NHr0aLswcaMqVapo/vz5+V0aAAAAgOsU+O+xAAAAAFDwESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAMCE93XB0CfnGmcYC4M4r0I+bBQCgoHN1ddGy93/VuTPJji7FlAqViuuZ5+o5ugwAhRjBAgAAk86dSdapE0mOLgMAHIpLoQAAAACYRrAAAAB55kz3ZTjTWABH4FIoAACQZ9xjAiADwQIAAJjCPSYAJC6FAgAAAJAPCBYAAAAATCNYAAAAADCNYAEAAADANIIFAABAHjnTI2rzMpa7ffywx1OhAAAA8uhuf9zu3T5+2CNYAAAAmHC3P273bh8//g+XQgEAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATLurgsXSpUvVvHlz+fn5qUuXLtq7d6+jSwIAAACcwl0TLD7//HNFRkbqxRdf1OrVq1WnTh317dtXCQkJji4NAAAAKPTummCxcOFCde3aVZ07d1bt2rU1duxYFStWTKtWrXJ0aQAAAEChd1cEi5SUFB04cEAhISG2NldXV4WEhGj37t0OrAwAAABwDkUcXcCd8Pfff8tqtap8+fJ27eXLl9fRo0dvubxhGJL+CShubm6Z5ru5ualSZQ9lMatQ8a7oIavVKqvVmqvlGD/jd4bxS3nbBnf7+CXn2QaMn/EzfsZ/t45fyn4bZExnHA/fjIuRk16F3NmzZ/Xwww9rxYoVCgoKsrVPmTJFP/74oz755JObLp+SkqJ9+/bd7jIBAACAAsnPz0/u7u437XNXnLEoW7as3NzcMt2onZCQIC8vr1suX6RIEfn5+cnV1VUuLi63q0wAAACgQDEMQ+np6SpS5Nax4a4IFu7u7qpfv75iYmL0yCOPSJLS09MVExOjHj163HJ5V1fXWyY0AAAA4G52VwQLSXr22Wc1YsQI+fr6yt/fXx9++KGuXLmiTp06Obo0AAAAoNC7a4LFo48+qvPnz2vWrFmKi4tT3bp19d577+XoUigAAAAAN3dX3LwNAAAA4Pa6K77HAgAAAMDtRbAAAAAAYBrBAgAAAIBpBAsAAAAAphEsCoilS5eqefPm8vPzU5cuXbR3796b9v/iiy/Upk0b+fn5qX379vr+++/vUKX578cff9TAgQMVGhoqHx8fbdy48ZbL7NixQx07dpSvr69atmyp6OjoO1Dp7TFv3jx17txZQUFBCg4O1qBBg3T06NFbLucs+8CyZcvUvn17NWjQQA0aNFC3bt1uORZnGXtW/ve//8nHx0dvvfXWTfs5yzaYPXu2fHx87H7atGlz02WcZewZzp49q2HDhqlx48by9/dX+/bttW/fvpsu40zvgc2bN8+0D/j4+Gjs2LHZLuNM+4DVatXMmTPVvHlz+fv765FHHtGcOXN0q2frONM+kJSUpLfeekvNmjWTv7+/nnrqqVseBxXW8d/qmMcwDP33v/9VaGio/P391adPH/3555+3XG9ujyNvGwMOt379eqN+/frGypUrjcOHDxujRo0yGjVqZMTHx2fZf9euXUbdunWN+fPnG0eOHDFmzJhh1K9f34iNjb3DleePTZs2GdOnTze++uorw2KxGF9//fVN+x8/ftwICAgwIiMjjSNHjhiLFy826tata2zevPkOVZy/nnvuOWPVqlXGb7/9Zhw8eNDo16+f0bRpU+Py5cvZLuNM+8A333xjbNq0yfjjjz+Mo0ePGtOnTzfq169v/Pbbb1n2d6ax3+iXX34xmjVrZrRv396YMGFCtv2caRvMmjXLaNeunXHu3DnbT0JCQrb9nWnshmEYFy5cMJo1a2ZEREQYv/zyi3H8+HFjy5YtxrFjx7JdxtneAxMSEux+/z/88INhsViM7du3Z9nf2faBuXPnGg8++KDx3XffGSdOnDC++OILIzAw0Pjwww+zXcbZ9oGhQ4cajz76qLFz507jzz//NGbNmmU0aNDAOHPmTJb9C/P4b3XMM2/ePKNhw4bG119/bRw8eNAYOHCg0bx5c+Pq1avZrjO3x5G3E8GiAHjyySeNsWPH2qatVqsRGhpqzJs3L8v+Q4cONfr372/X1qVLF+M///nPba3zTshJsJgyZYrRrl07u7aXX37ZeO65525naXdMQkKCYbFYjJ07d2bbx5n3AcMwjAceeMD4+OOPs5znrGNPSkoyWrVqZfzwww9Gjx49bhosnGkbzJo1y3j88cdz3N+Zxm4YhvH2228bTz/9dK6Wcfb3wAkTJhiPPPKIkZ6enuV8Z9sH+vfvb7z++ut2bYMHDzZee+21bJdxpn3gypUrRt26dY3vvvvOrr1jx47G9OnTs1zGWcZ/4zFPenq60aRJE+O9996ztV26dMnw9fU1Pvvss2zXk9vjyNuJS6EcLCUlRQcOHFBISIitzdXVVSEhIdq9e3eWy+zZs0fBwcF2baGhodqzZ8/tLLXAcPbxJyYmSpI8PT2z7eOs28BqtWr9+vVKTk5WUFBQln2cdezjxo1TeHi43XtBdpxtGxw7dkyhoaFq0aKFXnvtNZ0+fTrbvs429m+//Va+vr4aMmSIgoOD9cQTT+jjjz++6TLOtg2ul5KSorVr16pz585ycXHJso+zjT8oKEjbt2/XH3/8IUk6dOiQdu3apYcffjjbZZxpG6Slpclqteqee+6xa7/nnnv0888/Z7mMM43/eidPnlRcXJzd/wOlSpVSQEBAtseEeTmOvJ3umm/eLqj+/vtvWa1WlS9f3q69fPny2V5nHx8fn+kbw8uXL6/4+PjbVmdBktX4vby8lJSUpKtXr6pYsWIOqsy89PR0TZw4UQ0aNJDFYsm2n7PtA7GxsXrqqad07do1FS9eXHPmzFHt2rWz7OtsY5ek9evX69dff9XKlStz1N+ZtoG/v78iIyNVs2ZNxcXFac6cOerevbvWrVunkiVLZurvTGOXpBMnTmj58uV69tlnNXDgQO3bt08TJkxQ0aJF1bFjxyyXceb3wI0bNyoxMTHbsUvOtw/0799fSUlJatu2rdzc3GS1WvXKK6/o8ccfz3YZZ9oHSpYsqaCgIL377ru677775OXlpc8++0x79uxR9erVs1zGmcZ/vbi4OEnK8pgwu/07L8eRtxPBAihAxo4dq8OHD2vZsmWOLuWOqlmzptasWaPExERt2LBBI0aM0JIlS7INF87kr7/+0ltvvaX3338/0yd2d4Pw8HDbv+vUqaOAgAA1a9ZMX3zxhbp06eLAyu4MwzDk6+urV199VZJUr149HT58WCtWrLjpwbWzWrVqlR5++GFVrFjR0aXcMV988YXWrVunadOmqXbt2jp48KAiIyNVoUKFu2YfmDJlikaOHKmHH35Ybm5uqlevntq1a6cDBw44ujTkEsHCwcqWLSs3NzclJCTYtSckJGRK4xm8vLwyJdeb9Xc2WY0/Pj5eJUuWLLSfUkj/XAqzadMmLVmyRJUqVbppX2fbB9zd3VWjRg1Jkq+vr/bt26dFixZp3Lhxmfo629gPHDighIQEderUydZmtVr1448/aunSpdq3b5/c3NzslnG2bXC90qVL61//+peOHz+e5XxnG7u3t7dq1apl13bfffdpw4YN2S7jrO+Bp06d0rZt2zR79uyb9nO2fWDKlCnq37+/2rVrJ0ny8fHR6dOnNW/evGyDhbPtA9WrV9eSJUuUnJyspKQkVahQQS+//LKqVauWZX9nG38Gb29vSf/szxUqVLC1JyQkqE6dOlkuk5fjyNuJeywczN3dXfXr11dMTIytLT09XTExMdleYx4YGKjt27fbtW3btk2BgYG3s9QCw9nGbxiGxo0bp6+//loffvhhtm+k13O2bXCj9PR0paSkZDnP2cb+0EMPad26dVqzZo3tx9fXV+3bt9eaNWsyhQrJ+bbB9S5fvqwTJ07Y/oO9kbONvUGDBrZr6zP8+eefqlKlSrbLONs2yBAdHa3y5curadOmN+3nbOO/evVqpvtJ3Nzcbvq4WWfbBhmKFy+uChUq6OLFi9q6datatGiRZT9nHX/VqlXl7e1td0yYlJSkX375JdtjwrwcR95Wd/x2cWSyfv16w9fX14iOjjaOHDli/Oc//zEaNWpkxMXFGYZhGP/+97+NqVOn2vrv2rXLqFevnrFgwQLjyJEjxqxZswr1o/aSkpKMX3/91fj1118Ni8ViLFy40Pj111+NU6dOGYZhGFOnTjX+/e9/2/pnPGZu8uTJxpEjR4wlS5YUmsfMZWX06NFGw4YNjR07dtg9cvHKlSu2Ps68D0ydOtXYuXOnceLECePQoUPG1KlTDR8fH2Pr1q2GYTj32LNz41OhnHkbTJo0ydixY4dx4sQJY9euXUafPn2Mxo0b2x4568xjN4x/HjFcr149Y+7cucaff/5prF271ggICDA+/fRTWx9nfw80jH+eYtO0aVPj7bffzjTP2feBESNGGGFhYbbHzX711VdG48aNjSlTptj6OPs+sHnzZuP77783jh8/bmzdutV4/PHHjS5duhgpKSmGYTjX+G91zDNv3jyjUaNGxsaNG41Dhw4ZL7zwQqbHzfbq1ctYvHixbfpWx5F3EsGigFi8eLHRtGlTo379+saTTz5p7NmzxzavR48exogRI+z6f/7550arVq2M+vXrG+3atTM2bdp0p0vON9u3bzcsFkumn4wxjxgxwujRo0emZTp06GDUr1/faNGihbFq1SpHlJ4vshq7xWKxG5Mz7wOvv/660axZM6N+/frGQw89ZPTu3dsWKgzDuceenRuDhTNvg5dfftlo0qSJUb9+fSMsLMx4+eWX7b7DwZnHnuHbb781HnvsMcPX19do06aN8dFHH9nNd/b3QMMwjC1bthgWi8U4evRopnnOvg8kJiYaEyZMMJo2bWr4+fkZLVq0MKZPn25cu3bN1sfZ94H169cbLVq0MOrXr280adLEGDt2rHHp0iXbfGca/62OedLT042ZM2caISEhhq+vr9G7d+9MfxfNmjUzZs2aZdd2s+PIO8nFMG7x1Y4AAAAAcAvcYwEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABALCJiIjQoEGDHF0G/r8dO3bIx8dHly5dcnQpAHBLBAsAKGAiIiLk4+OT6adv376OLq1QyelBeUE5eO/Zs6feeusth9YAAGYUcXQBAIDMwsLCFBkZadfm7u7uoGoAALg1zlgAQAHk7u4ub29vux9PT0/bfB8fH61YsUIDBgxQQECA2rZtq927d+vYsWPq2bOnAgMD9dRTT+n48eO2ZWbPnq0OHTpoxYoVCg8PV0BAgIYOHarExMRs60hJSdGECRMUHBwsPz8/Pf3009q7d68kyTAMtWzZUgsWLLBb5uDBg/Lx8dGxY8fyXKskbdy4UR07dpSfn59atGihd955R2lpaXbb4JNPPtGLL76ogIAAtWrVSt98840k6eTJk+rVq5ck6YEHHpCPj48iIiLy8qtQSkqKJk+erLCwMAUGBqpLly7asWOHbX50dLQaNWqkLVu2qG3btgoKClLfvn117tw5W5+0tDRNmDBBjRo1UuPGjfX2229rxIgRtsvOIiIitHPnTi1atMh2hurkyZO25Q8cOKBOnTopICBATz31lI4ePZqnsQDA7USwAIBC6t1331WHDh20Zs0a3XfffXrttdf05ptvqn///lq1apUMw9C4cePsljl+/Li++OILRUVF6b333tPBgwc1ZsyYbF9jypQp2rBhgyZNmqTVq1erRo0aev7553XhwgW5uLioc+fOio6Otltm1apVeuCBB1SjRo081/rTTz9pxIgR6tWrlz7//HONGzdO0dHRioqKsnutd955R23bttXatWv18MMPa9iwYbpw4YLuvfdezZ49W5L05ZdfauvWrXrjjTfytJ3HjRun3bt3a8aMGVq7dq3atGmj559/Xn/++aetz9WrV/X+++9rypQpWrJkif766y9NnjzZNn/+/Plat26dIiMjtWzZMiUlJWnjxo22+W+88YaCgoLUtWtXbd26VVu3btW9995rmz9jxgxFRERo1apVcnNz08iRI/M0FgC4rQwAQIEyYsQIo27dukZgYKDdz9y5c219LBaLMWPGDNv07t27DYvFYnzyySe2ts8++8zw8/OzTc+aNcuoW7eucebMGVvb999/b9SpU8c4d+6c7bVfeOEFwzAM4/Lly0b9+vWNtWvX2vqnpKQYoaGhxvz58w3DMIwzZ84YdevWNX755Rfb/MaNGxvR0dGmau3du7cRFRVlt13WrFljNGnSJNv1Xr582bBYLMb3339vGIZhbN++3bBYLMbFixczbePr3azfqVOnMm2zjPqmTZtmGIZhrFq1yrBYLMaxY8ds85csWWKEhITYpkNCQoz33nvPNp2WlmY0bdrUtq0NwzB69OhhTJgwIcvatm3bZmvbtGmTYbFYjKtXr950XABwp3GPBQAUQI0bN850JuH6S6Gkfy4FylC+fHlJksVisWu7du2akpKSVLJkSUnSvffeq4oVK9r6BAUFKT09XX/88Ye8vb3t1n/8+HGlpqaqQYMGtraiRYvK399fv//+uySpYsWKCg8P18qVK+Xv76/vvvtOKSkpatOmjalaDx06pJ9//tnuDIXVatW1a9d05coVeXh4ZFpv8eLFVbJkSZ0/f1755bfffpPVas00npSUFJUpU8Y27eHhoerVq9umK1SooISEBElSYmKi4uPj5e/vb5vv5uam+vXrKz09PUd1XD/OjN9TQkKCKleunOsxAcDtQrAAgALIw8PD7lKirBQtWtT2bxcXl2zbcnrwmlddunTR8OHDNXLkSEVHR+vRRx+1Hfjntdbk5GS99NJLatWqVabXu+eee7Jcb8Z68nO8ycnJcnNzs12CdL3ixYvb/l2kiP1/py4uLjIMI9/quH79d+r3CgC5RbAAgLvIX3/9pbNnz9rOWuzZs0eurq6qWbNmpr7Vq1dX0aJF9fPPP6tKlSqSpNTUVO3bt0+9e/e29QsPD5eHh4eWL1+uLVu2aMmSJabrrFevnv74449bhqubyQgdVqs1z+uoW7eurFarzp8/r0aNGuVpHaVKlZKXl5f27dunBx54wFbTr7/+qjp16tjVS1gAUJgRLACgAEpJSVFcXJxdm5ubm8qVK2dqvffcc48iIiI0YsQIJSUlacKECWrbtm2my6Ckfz6Rf/rppzVlyhR5enqqcuXKeu+993T16lU9+eSTdnV16tRJ06ZNU40aNRQUFGSqRkl68cUXNXDgQFWuXFmtW7eWq6urDh06pN9++02vvPJKjtZRpUoVubi4aNOmTQoPD9c999yjEiVKZNv/t99+s5vv4uKiOnXqqH379ho+fLgiIiJUt25d/f3334qJiZGPj4+aNm2ao1p69OihefPmqXr16rrvvvu0ZMkSXbx40Xb2IaPeX375RSdPnlTx4sXtLrUCgMKAYAEABdCWLVsUGhpq11azZk19+eWXptZbvXp1tWzZUv369dPFixfVtGlTjR49Otv+w4YNk2EYGj58uC5fvixfX1+99957me73ePLJJxUVFaVOnTqZqi9DWFiYoqKiNGfOHM2fP19FihTRfffdpy5duuR4HRUrVtRLL72kadOm6fXXX9cTTzyhSZMmZdu/e/fudtNubm769ddfFRkZqblz52rSpEk6d+6cypQpo8DAwByHCknq16+f4uPjNWLECLm5ualr164KDQ21u7zqueeeU0REhNq1a6erV6/aHp0LAIWFi5GfF4ECAAqs2bNna+PGjfr000/zfd0//fST+vTpo02bNsnLyyvf1+9s0tPT1bZtW7Vt21Yvv/yyo8sBgHzBGQsAQJ6lpKTo/Pnzmj17tlq3bk2oyMapU6f0ww8/6IEHHlBKSoqWLl2qU6dOqX379o4uDQDyDV+QBwDIs88++0zNmjVTYmKihg8f7uhyCixXV1dFR0frySef1NNPP63ffvtNCxcuVK1atRxdGgDkGy6FAgAAAGAaZywAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJj2/wBnla2Fs59WwwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Employment length distribution\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.countplot(x=\"emp_length\", data=df, color=\"slateblue\")\n",
        "plt.title(\"Distribution of Employment Length (years)\")\n",
        "plt.xlabel(\"Employment Length\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cca5aa67",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "cca5aa67",
        "outputId": "dcadf400-394f-4cad-df68-59392874bc50"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Interest rate compared against default risk\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.boxplot(x=\"default_risk\", y=\"interest_rate\", data=df, palette=[\"seagreen\", \"indianred\"])\n",
        "plt.title(\"Interest Rate by Default Risk\")\n",
        "plt.xlabel(\"Default Risk (0 = No, 1 = Yes)\")\n",
        "plt.ylabel(\"Interest Rate\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "504685ce",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "504685ce",
        "outputId": "7ee1d2bf-b332-4a84-85b4-f32ecc76beda"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Debt to income ratio compared against default risk\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.boxplot(x=\"default_risk\", y=\"debt_to_income\", data=df, palette=[\"seagreen\", \"indianred\"])\n",
        "plt.title(\"Debt to Income Ratio by Default Risk\")\n",
        "plt.xlabel(\"Default Risk (0 = No, 1 = Yes)\")\n",
        "plt.ylabel(\"Debt to Income Ratio\")\n",
        "plt.ylim(0, 100)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "63d4bbe2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 907
        },
        "id": "63d4bbe2",
        "outputId": "4169866f-e5af-458c-c96c-9a21e912d177"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x900 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Correlation heatmap for numerical features against the target\n",
        "correlation_columns = [\n",
        "    \"loan_amount\", \"term\", \"interest_rate\", \"installment\",\n",
        "    \"annual_income\", \"debt_to_income\", \"delinq_2y\",\n",
        "    \"inquiries_last_12m\", \"total_credit_lines\", \"open_credit_lines\",\n",
        "    \"total_credit_limit\", \"total_credit_utilized\", \"account_never_delinq_percent\",\n",
        "    \"default_risk\"\n",
        "]\n",
        "\n",
        "plt.figure(figsize=(12, 9))\n",
        "correlation_matrix = df[correlation_columns].corr()\n",
        "sns.heatmap(correlation_matrix, annot=True, fmt=\".2f\", cmap=\"coolwarm\", center=0)\n",
        "plt.title(\"Correlation Heatmap of Key Numerical Features\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "94ac4927",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "94ac4927",
        "outputId": "9fe7a7d0-7031-404a-a6c7-ae06abd2ecb2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>default_risk</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>interest_rate</th>\n",
              "      <td>0.096971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_purpose_house</th>\n",
              "      <td>0.045529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>installment</th>\n",
              "      <td>0.032533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>verified_income_Verified</th>\n",
              "      <td>0.031435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>issue_month_Jan-2018</th>\n",
              "      <td>0.030193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inquiries_last_12m</th>\n",
              "      <td>0.027271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>term</th>\n",
              "      <td>0.025385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_amount</th>\n",
              "      <td>0.022732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>homeownership_OWN</th>\n",
              "      <td>0.022303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>application_type</th>\n",
              "      <td>0.021203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_joint_application</th>\n",
              "      <td>0.021203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_purpose_medical</th>\n",
              "      <td>0.018942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state_freq</th>\n",
              "      <td>0.016228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_purpose_major_purchase</th>\n",
              "      <td>0.016035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>homeownership_RENT</th>\n",
              "      <td>0.010493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annual_income</th>\n",
              "      <td>0.009592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_purpose_home_improvement</th>\n",
              "      <td>0.009081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>verified_income_Source Verified</th>\n",
              "      <td>0.007696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>delinq_2y</th>\n",
              "      <td>0.007509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_purpose_moving</th>\n",
              "      <td>0.007107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_credit_utilized</th>\n",
              "      <td>0.006192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>earliest_credit_line</th>\n",
              "      <td>0.005549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accounts_opened_24m</th>\n",
              "      <td>0.004374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_collections_last_12m</th>\n",
              "      <td>0.003306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>months_since_last_delinq</th>\n",
              "      <td>0.002289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_purpose_other</th>\n",
              "      <td>0.002109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_purpose_vacation</th>\n",
              "      <td>-0.000967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>current_installment_accounts</th>\n",
              "      <td>-0.001289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>current_accounts_delinq</th>\n",
              "      <td>-0.001346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_accounts_30d_past_due</th>\n",
              "      <td>-0.001346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_purpose_small_business</th>\n",
              "      <td>-0.001371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emp_length</th>\n",
              "      <td>-0.002176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tax_liens</th>\n",
              "      <td>-0.003426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_purpose_renewable_energy</th>\n",
              "      <td>-0.004257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_collection_amount_ever</th>\n",
              "      <td>-0.005466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_purpose_debt_consolidation</th>\n",
              "      <td>-0.006161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_historical_failed_to_pay</th>\n",
              "      <td>-0.007293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_credit_limit</th>\n",
              "      <td>-0.007519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>initial_listing_status</th>\n",
              "      <td>-0.008355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>public_record_bankrupt</th>\n",
              "      <td>-0.008847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_debit_limit</th>\n",
              "      <td>-0.010219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>account_never_delinq_percent</th>\n",
              "      <td>-0.011473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>debt_to_income</th>\n",
              "      <td>-0.011664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_cc_carrying_balance</th>\n",
              "      <td>-0.013017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_mort_accounts</th>\n",
              "      <td>-0.015068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>months_since_90d_late</th>\n",
              "      <td>-0.015443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_active_debit_accounts</th>\n",
              "      <td>-0.016446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>months_since_last_credit_inquiry</th>\n",
              "      <td>-0.022276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_credit_lines</th>\n",
              "      <td>-0.022366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_satisfactory_accounts</th>\n",
              "      <td>-0.022558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_credit_lines</th>\n",
              "      <td>-0.022577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>disbursement_method</th>\n",
              "      <td>-0.022588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>issue_month_Mar-2018</th>\n",
              "      <td>-0.023649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_open_cc_accounts</th>\n",
              "      <td>-0.024316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_total_cc_accounts</th>\n",
              "      <td>-0.025988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_purpose_credit_card</th>\n",
              "      <td>-0.028759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_accounts_120d_past_due</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ],
            "text/plain": [
              "interest_rate                       0.096971\n",
              "loan_purpose_house                  0.045529\n",
              "installment                         0.032533\n",
              "verified_income_Verified            0.031435\n",
              "issue_month_Jan-2018                0.030193\n",
              "inquiries_last_12m                  0.027271\n",
              "term                                0.025385\n",
              "loan_amount                         0.022732\n",
              "homeownership_OWN                   0.022303\n",
              "application_type                    0.021203\n",
              "is_joint_application                0.021203\n",
              "loan_purpose_medical                0.018942\n",
              "state_freq                          0.016228\n",
              "loan_purpose_major_purchase         0.016035\n",
              "homeownership_RENT                  0.010493\n",
              "annual_income                       0.009592\n",
              "loan_purpose_home_improvement       0.009081\n",
              "verified_income_Source Verified     0.007696\n",
              "delinq_2y                           0.007509\n",
              "loan_purpose_moving                 0.007107\n",
              "total_credit_utilized               0.006192\n",
              "earliest_credit_line                0.005549\n",
              "accounts_opened_24m                 0.004374\n",
              "num_collections_last_12m            0.003306\n",
              "months_since_last_delinq            0.002289\n",
              "loan_purpose_other                  0.002109\n",
              "loan_purpose_vacation              -0.000967\n",
              "current_installment_accounts       -0.001289\n",
              "current_accounts_delinq            -0.001346\n",
              "num_accounts_30d_past_due          -0.001346\n",
              "loan_purpose_small_business        -0.001371\n",
              "emp_length                         -0.002176\n",
              "tax_liens                          -0.003426\n",
              "loan_purpose_renewable_energy      -0.004257\n",
              "total_collection_amount_ever       -0.005466\n",
              "loan_purpose_debt_consolidation    -0.006161\n",
              "num_historical_failed_to_pay       -0.007293\n",
              "total_credit_limit                 -0.007519\n",
              "initial_listing_status             -0.008355\n",
              "public_record_bankrupt             -0.008847\n",
              "total_debit_limit                  -0.010219\n",
              "account_never_delinq_percent       -0.011473\n",
              "debt_to_income                     -0.011664\n",
              "num_cc_carrying_balance            -0.013017\n",
              "num_mort_accounts                  -0.015068\n",
              "months_since_90d_late              -0.015443\n",
              "num_active_debit_accounts          -0.016446\n",
              "months_since_last_credit_inquiry   -0.022276\n",
              "total_credit_lines                 -0.022366\n",
              "num_satisfactory_accounts          -0.022558\n",
              "open_credit_lines                  -0.022577\n",
              "disbursement_method                -0.022588\n",
              "issue_month_Mar-2018               -0.023649\n",
              "num_open_cc_accounts               -0.024316\n",
              "num_total_cc_accounts              -0.025988\n",
              "loan_purpose_credit_card           -0.028759\n",
              "num_accounts_120d_past_due               NaN\n",
              "Name: default_risk, dtype: float64"
            ]
          },
          "execution_count": 97,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Correlation of all numerical features with the target, ranked\n",
        "target_correlations = df.corr(numeric_only=True)[\"default_risk\"].sort_values(ascending=False)\n",
        "target_correlations.drop(\"default_risk\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "47d9d96d",
      "metadata": {
        "id": "47d9d96d"
      },
      "source": [
        "# Bias and Fairness Exploration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "82b8c728",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "82b8c728",
        "outputId": "5a123740-8dc5-4be9-b617-99dc627c7bec"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Default risk rate by home ownership status\n",
        "homeownership_columns = [c for c in df.columns if c.startswith(\"homeownership_\")]\n",
        "\n",
        "homeownership_summary = df.groupby(\n",
        "    df[homeownership_columns].idxmax(axis=1)\n",
        ")[\"default_risk\"].mean().sort_values(ascending=False)\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "homeownership_summary.plot(kind=\"bar\", color=\"darkorange\")\n",
        "plt.title(\"Default Risk Rate by Home Ownership Status\")\n",
        "plt.xlabel(\"Home Ownership Category\")\n",
        "plt.ylabel(\"Default Risk Rate\")\n",
        "plt.xticks(rotation=30, ha=\"right\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b9cf1acb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "b9cf1acb",
        "outputId": "4b51f4b2-5ef8-4f86-bf8e-4f8d6935d2d9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Explore default risk across annual income groups as part of the fairness assessment.\n",
        "\n",
        "df[\"income_band\"] = pd.qcut(\n",
        "    df[\"annual_income\"],\n",
        "    q=4,\n",
        "    labels=[\"Low\", \"Medium\", \"High\", \"Very High\"]\n",
        ")\n",
        "\n",
        "income_band_summary = df.groupby(\"income_band\")[\"default_risk\"].mean()\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "\n",
        "income_band_summary.plot(\n",
        "    kind=\"bar\",\n",
        "    color=\"mediumpurple\"\n",
        ")\n",
        "\n",
        "plt.title(\"Default Risk Rate by Income Band\")\n",
        "plt.xlabel(\"Income Band\")\n",
        "plt.ylabel(\"Default Risk Rate\")\n",
        "plt.xticks(rotation=0)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "df = df.drop(columns=[\"income_band\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "da68d23b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "da68d23b",
        "outputId": "338132e2-0859-45e1-a089-426a8ff7fc4a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Default risk rate by verified income status\n",
        "verified_columns = [c for c in df.columns if c.startswith(\"verified_income_\")]\n",
        "\n",
        "if len(verified_columns) > 0:\n",
        "    verified_summary = df.groupby(\n",
        "        df[verified_columns].idxmax(axis=1)\n",
        "    )[\"default_risk\"].mean().sort_values(ascending=False)\n",
        "\n",
        "    plt.figure(figsize=(8, 5))\n",
        "    verified_summary.plot(kind=\"bar\", color=\"teal\")\n",
        "    plt.title(\"Default Risk Rate by Income Verification Status\")\n",
        "    plt.xlabel(\"Verification Status\")\n",
        "    plt.ylabel(\"Default Risk Rate\")\n",
        "    plt.xticks(rotation=30, ha=\"right\")\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1ad1e24d",
      "metadata": {
        "id": "1ad1e24d"
      },
      "source": [
        "# Train-Test Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8b425bb5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8b425bb5",
        "outputId": "94fa566f-03f7-46ca-bb33-80f58be59c01"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Feature matrix shape: (9976, 57)\n",
            "Target vector shape: (9976,)\n"
          ]
        }
      ],
      "source": [
        "# Separate features and target\n",
        "X = df.drop(columns=[\"default_risk\"])\n",
        "y = df[\"default_risk\"]\n",
        "\n",
        "print(\"Feature matrix shape:\", X.shape)\n",
        "print(\"Target vector shape:\", y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "369baf50",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "369baf50",
        "outputId": "e41e3e83-461d-4ba0-ef4f-b9ca7fb0cd2e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training set shape: (7980, 57)\n",
            "Test set shape: (1996, 57)\n",
            "\n",
            "Training set default rate: 1.78 percent\n",
            "Test set default rate: 1.75 percent\n"
          ]
        }
      ],
      "source": [
        "# Stratified train-test split, 80 percent train, 20 percent test\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y,\n",
        "    test_size=0.20,\n",
        "    stratify=y,\n",
        "    random_state=RANDOM_STATE\n",
        ")\n",
        "\n",
        "print(\"Training set shape:\", X_train.shape)\n",
        "print(\"Test set shape:\", X_test.shape)\n",
        "print()\n",
        "print(\"Training set default rate:\", round(y_train.mean() * 100, 2), \"percent\")\n",
        "print(\"Test set default rate:\", round(y_test.mean() * 100, 2), \"percent\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "23d36b78",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23d36b78",
        "outputId": "f75a148a-1b42-431a-9af5-b071f9394b5f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Feature scaling complete.\n"
          ]
        }
      ],
      "source": [
        "# Standardize the feature set while preserving feature names for later interpretation.\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "X_train_scaled = pd.DataFrame(\n",
        "    X_train_scaled,\n",
        "    columns=X_train.columns,\n",
        "    index=X_train.index\n",
        ")\n",
        "\n",
        "X_test_scaled = pd.DataFrame(\n",
        "    X_test_scaled,\n",
        "    columns=X_test.columns,\n",
        "    index=X_test.index\n",
        ")\n",
        "\n",
        "print(\"Feature scaling complete.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aa374313",
      "metadata": {
        "id": "aa374313"
      },
      "source": [
        "# Logistic Regression Model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "42fab6b0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "42fab6b0",
        "outputId": "5a25d05f-b520-4247-c38b-3d006cb75dee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Baseline Logistic Regression trained.\n"
          ]
        }
      ],
      "source": [
        "# Train a baseline Logistic Regression model\n",
        "log_reg_baseline = LogisticRegression(\n",
        "    class_weight=\"balanced\",\n",
        "    max_iter=1000,\n",
        "    random_state=RANDOM_STATE\n",
        ")\n",
        "\n",
        "log_reg_baseline.fit(X_train_scaled, y_train)\n",
        "\n",
        "y_pred_lr_baseline = log_reg_baseline.predict(X_test_scaled)\n",
        "y_proba_lr_baseline = log_reg_baseline.predict_proba(X_test_scaled)[:, 1]\n",
        "\n",
        "print(\"Baseline Logistic Regression trained.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7a6a2f3a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7a6a2f3a",
        "outputId": "e9410757-4838-4f5d-90c2-76cec1b5b8c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.72      0.84      1961\n",
            "           1       0.03      0.54      0.06        35\n",
            "\n",
            "    accuracy                           0.72      1996\n",
            "   macro avg       0.51      0.63      0.45      1996\n",
            "weighted avg       0.97      0.72      0.82      1996\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Classification report for the baseline model\n",
        "print(classification_report(y_test, y_pred_lr_baseline))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6afc55d8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "6afc55d8",
        "outputId": "b60b706b-8c06-46d1-eb7f-5d55a0329e95"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Confusion matrix for the baseline model\n",
        "cm_lr_baseline = confusion_matrix(y_test, y_pred_lr_baseline)\n",
        "\n",
        "plt.figure(figsize=(6, 5))\n",
        "sns.heatmap(cm_lr_baseline, annot=True, fmt=\"d\", cmap=\"Blues\",\n",
        "            xticklabels=[\"No Default\", \"Default\"],\n",
        "            yticklabels=[\"No Default\", \"Default\"])\n",
        "plt.title(\"Confusion Matrix: Logistic Regression (Baseline)\")\n",
        "plt.xlabel(\"Predicted Label\")\n",
        "plt.ylabel(\"True Label\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e7701a41",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "e7701a41",
        "outputId": "3bca0276-b5d5-45c5-d898-52f3daa026b1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Baseline ROC-AUC: 0.687\n"
          ]
        }
      ],
      "source": [
        "# ROC curve and ROC-AUC for the baseline model\n",
        "fpr_lr, tpr_lr, _ = roc_curve(y_test, y_proba_lr_baseline)\n",
        "auc_lr_baseline = roc_auc_score(y_test, y_proba_lr_baseline)\n",
        "\n",
        "plt.figure(figsize=(7, 6))\n",
        "plt.plot(fpr_lr, tpr_lr, color=\"steelblue\", label=f\"Logistic Regression (AUC = {auc_lr_baseline:.3f})\")\n",
        "plt.plot([0, 1], [0, 1], color=\"grey\", linestyle=\"--\", label=\"Random Classifier\")\n",
        "plt.title(\"ROC Curve: Logistic Regression (Baseline)\")\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(\"Baseline ROC-AUC:\", round(auc_lr_baseline, 4))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "23d84684",
      "metadata": {
        "id": "23d84684"
      },
      "source": [
        "# Logistic Regression Hyperparameter Tuning\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dff896d4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dff896d4",
        "outputId": "66bca4bb-fea2-4de9-bbaa-ee70073dbdbc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best parameters: {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "Best cross-validated ROC-AUC: 0.6802\n"
          ]
        }
      ],
      "source": [
        "# Define the parameter grid for Logistic Regression\n",
        "lr_param_grid = {\n",
        "    \"C\": [0.01, 0.1, 1, 10],\n",
        "    \"penalty\": [\"l2\"],\n",
        "    \"solver\": [\"lbfgs\"]\n",
        "}\n",
        "\n",
        "stratified_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)\n",
        "\n",
        "lr_grid_search = GridSearchCV(\n",
        "    estimator=LogisticRegression(class_weight=\"balanced\", max_iter=1000, random_state=RANDOM_STATE),\n",
        "    param_grid=lr_param_grid,\n",
        "    scoring=\"roc_auc\",\n",
        "    cv=stratified_cv,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "lr_grid_search.fit(X_train_scaled, y_train)\n",
        "\n",
        "print(\"Best parameters:\", lr_grid_search.best_params_)\n",
        "print(\"Best cross-validated ROC-AUC:\", round(lr_grid_search.best_score_, 4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "15f7dab8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15f7dab8",
        "outputId": "b9a36024-f73f-4f5c-f2bd-3dbcf220edcb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.72      0.83      1961\n",
            "           1       0.03      0.51      0.06        35\n",
            "\n",
            "    accuracy                           0.72      1996\n",
            "   macro avg       0.51      0.62      0.45      1996\n",
            "weighted avg       0.97      0.72      0.82      1996\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Refit the tuned Logistic Regression model and evaluate on the test set\n",
        "log_reg_tuned = lr_grid_search.best_estimator_\n",
        "\n",
        "y_pred_lr = log_reg_tuned.predict(X_test_scaled)\n",
        "y_proba_lr = log_reg_tuned.predict_proba(X_test_scaled)[:, 1]\n",
        "\n",
        "print(classification_report(y_test, y_pred_lr))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e6d14cf2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e6d14cf2",
        "outputId": "fad69cbd-c2cc-4fe7-cd9d-61813aef01e0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'model': 'Logistic Regression',\n",
              " 'accuracy': 0.718436873747495,\n",
              " 'precision': 0.03197158081705151,\n",
              " 'recall': 0.5142857142857142,\n",
              " 'f1': 0.06020066889632107,\n",
              " 'roc_auc': np.float64(0.6862679390981278)}"
            ]
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Store Logistic Regression results for later comparison\n",
        "lr_results = {\n",
        "    \"model\": \"Logistic Regression\",\n",
        "    \"accuracy\": accuracy_score(y_test, y_pred_lr),\n",
        "    \"precision\": precision_score(y_test, y_pred_lr),\n",
        "    \"recall\": recall_score(y_test, y_pred_lr),\n",
        "    \"f1\": f1_score(y_test, y_pred_lr),\n",
        "    \"roc_auc\": roc_auc_score(y_test, y_proba_lr)\n",
        "}\n",
        "\n",
        "lr_results"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "628c1b5d",
      "metadata": {
        "id": "628c1b5d"
      },
      "source": [
        "# Random Forest Model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "64b14aba",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64b14aba",
        "outputId": "29dcad94-709e-41c6-ec32-f4707e3260b7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Baseline Random Forest trained.\n"
          ]
        }
      ],
      "source": [
        "# Train a baseline Random Forest model\n",
        "rf_baseline = RandomForestClassifier(\n",
        "    class_weight=\"balanced\",\n",
        "    random_state=RANDOM_STATE,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "rf_baseline.fit(X_train, y_train)\n",
        "\n",
        "y_pred_rf_baseline = rf_baseline.predict(X_test)\n",
        "y_proba_rf_baseline = rf_baseline.predict_proba(X_test)[:, 1]\n",
        "\n",
        "print(\"Baseline Random Forest trained.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "547ffe02",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "547ffe02",
        "outputId": "62c5816a-2708-4d02-9537-dc1787323bfc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99      1961\n",
            "           1       0.00      0.00      0.00        35\n",
            "\n",
            "    accuracy                           0.98      1996\n",
            "   macro avg       0.49      0.50      0.50      1996\n",
            "weighted avg       0.97      0.98      0.97      1996\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Classification report for the baseline Random Forest model\n",
        "print(classification_report(y_test, y_pred_rf_baseline))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a3679a3d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "a3679a3d",
        "outputId": "b394356d-3bf9-4c18-fd19-746220d37b68"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Confusion matrix for the baseline Random Forest model\n",
        "cm_rf_baseline = confusion_matrix(y_test, y_pred_rf_baseline)\n",
        "\n",
        "plt.figure(figsize=(6, 5))\n",
        "sns.heatmap(cm_rf_baseline, annot=True, fmt=\"d\", cmap=\"Greens\",\n",
        "            xticklabels=[\"No Default\", \"Default\"],\n",
        "            yticklabels=[\"No Default\", \"Default\"])\n",
        "plt.title(\"Confusion Matrix: Random Forest (Baseline)\")\n",
        "plt.xlabel(\"Predicted Label\")\n",
        "plt.ylabel(\"True Label\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d28ea3bb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "d28ea3bb",
        "outputId": "8c0ed735-d4bd-46bd-8919-5a7953aa55b8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tuned ROC-AUC: 0.6572\n"
          ]
        }
      ],
      "source": [
        "# ROC curve and ROC-AUC for the tuned Random Forest model\n",
        "fpr_rf, tpr_rf, _ = roc_curve(y_test, y_proba_rf)\n",
        "auc_rf = roc_auc_score(y_test, y_proba_rf)\n",
        "\n",
        "plt.figure(figsize=(7, 6))\n",
        "plt.plot(fpr_rf, tpr_rf, color=\"seagreen\", label=f\"Random Forest (AUC = {auc_rf:.3f})\")\n",
        "plt.plot([0, 1], [0, 1], color=\"grey\", linestyle=\"--\", label=\"Random Classifier\")\n",
        "plt.title(\"ROC Curve: Random Forest (Tuned)\")\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(\"Tuned ROC-AUC:\", round(auc_rf, 4))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2cca689d",
      "metadata": {
        "id": "2cca689d"
      },
      "source": [
        "# Random Forest Hyperparameter Tuning\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3b250768",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3b250768",
        "outputId": "1dd5786c-b63d-4f69-c35b-6e7db54bfad9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best parameters: {'max_depth': 5, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
            "Best cross-validated ROC-AUC: 0.6951\n"
          ]
        }
      ],
      "source": [
        "# Define the parameter grid for Random Forest\n",
        "rf_param_grid = {\n",
        "    \"n_estimators\": [100, 200],\n",
        "    \"max_depth\": [5, 10, None],\n",
        "    \"min_samples_split\": [2, 5],\n",
        "    \"min_samples_leaf\": [1, 2]\n",
        "}\n",
        "\n",
        "rf_grid_search = GridSearchCV(\n",
        "    estimator=RandomForestClassifier(class_weight=\"balanced\", random_state=RANDOM_STATE, n_jobs=-1),\n",
        "    param_grid=rf_param_grid,\n",
        "    scoring=\"roc_auc\",\n",
        "    cv=stratified_cv,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "rf_grid_search.fit(X_train, y_train)\n",
        "\n",
        "print(\"Best parameters:\", rf_grid_search.best_params_)\n",
        "print(\"Best cross-validated ROC-AUC:\", round(rf_grid_search.best_score_, 4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3e60f860",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3e60f860",
        "outputId": "756eda73-1f61-43d9-9c60-310eed290e44"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.95      0.97      1961\n",
            "           1       0.04      0.11      0.06        35\n",
            "\n",
            "    accuracy                           0.94      1996\n",
            "   macro avg       0.51      0.53      0.51      1996\n",
            "weighted avg       0.97      0.94      0.95      1996\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Refit the tuned Random Forest model and evaluate on the test set\n",
        "rf_tuned = rf_grid_search.best_estimator_\n",
        "\n",
        "y_pred_rf = rf_tuned.predict(X_test)\n",
        "y_proba_rf = rf_tuned.predict_proba(X_test)[:, 1]\n",
        "\n",
        "print(classification_report(y_test, y_pred_rf))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "07107496",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07107496",
        "outputId": "84e0d15b-2dd5-44ce-fd65-c3c15976bb7c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'model': 'Random Forest',\n",
              " 'accuracy': 0.938877755511022,\n",
              " 'precision': 0.042105263157894736,\n",
              " 'recall': 0.11428571428571428,\n",
              " 'f1': 0.06153846153846154,\n",
              " 'roc_auc': np.float64(0.6572302760982007)}"
            ]
          },
          "execution_count": 117,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Store Random Forest results for later comparison\n",
        "rf_results = {\n",
        "    \"model\": \"Random Forest\",\n",
        "    \"accuracy\": accuracy_score(y_test, y_pred_rf),\n",
        "    \"precision\": precision_score(y_test, y_pred_rf),\n",
        "    \"recall\": recall_score(y_test, y_pred_rf),\n",
        "    \"f1\": f1_score(y_test, y_pred_rf),\n",
        "    \"roc_auc\": roc_auc_score(y_test, y_proba_rf)\n",
        "}\n",
        "\n",
        "rf_results"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "31b076f1",
      "metadata": {
        "id": "31b076f1"
      },
      "source": [
        "# Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "08fac0d6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "08fac0d6",
        "outputId": "2c27e4f0-9303-4fd5-b12e-9675b4791e45"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Combined ROC curve comparison\n",
        "plt.figure(figsize=(7, 6))\n",
        "plt.plot(fpr_lr, tpr_lr, color=\"steelblue\", label=f\"Logistic Regression (AUC = {lr_results['roc_auc']:.3f})\")\n",
        "plt.plot(fpr_rf, tpr_rf, color=\"seagreen\", label=f\"Random Forest (AUC = {rf_results['roc_auc']:.3f})\")\n",
        "plt.plot([0, 1], [0, 1], color=\"grey\", linestyle=\"--\", label=\"Random Classifier\")\n",
        "plt.title(\"ROC Curve Comparison: Logistic Regression vs Random Forest\")\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "155537a5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "155537a5",
        "outputId": "0b0b6071-13a2-41e1-9e9c-ba9f9d2f79a6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1300x500 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Side by side confusion matrices\n",
        "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n",
        "\n",
        "sns.heatmap(confusion_matrix(y_test, y_pred_lr), annot=True, fmt=\"d\", cmap=\"Blues\",\n",
        "            xticklabels=[\"No Default\", \"Default\"], yticklabels=[\"No Default\", \"Default\"], ax=axes[0])\n",
        "axes[0].set_title(\"Logistic Regression (Tuned)\")\n",
        "axes[0].set_xlabel(\"Predicted Label\")\n",
        "axes[0].set_ylabel(\"True Label\")\n",
        "\n",
        "sns.heatmap(confusion_matrix(y_test, y_pred_rf), annot=True, fmt=\"d\", cmap=\"Greens\",\n",
        "            xticklabels=[\"No Default\", \"Default\"], yticklabels=[\"No Default\", \"Default\"], ax=axes[1])\n",
        "axes[1].set_title(\"Random Forest (Tuned)\")\n",
        "axes[1].set_xlabel(\"Predicted Label\")\n",
        "axes[1].set_ylabel(\"True Label\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "33c65d7f",
      "metadata": {
        "id": "33c65d7f"
      },
      "source": [
        "# Model Comparison\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "685d51d6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "685d51d6",
        "outputId": "a98f4f8c-fa31-4402-af05-c041c1e2c404"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"comparison_df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Random Forest\",\n          \"Logistic Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15591704525163366,\n        \"min\": 0.7184,\n        \"max\": 0.9389,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9389,\n          0.7184\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0071417784899841285,\n        \"min\": 0.032,\n        \"max\": 0.0421,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0421,\n          0.032\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.282842712474619,\n        \"min\": 0.1143,\n        \"max\": 0.5143,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.1143,\n          0.5143\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0009192388155425136,\n        \"min\": 0.0602,\n        \"max\": 0.0615,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0615,\n          0.0602\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.020576807332528542,\n        \"min\": 0.6572,\n        \"max\": 0.6863,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.6572,\n          0.6863\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('comparison_df');\n",
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              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "                     accuracy  precision  recall      f1  roc_auc\n",
              "model                                                            \n",
              "Logistic Regression    0.7184     0.0320  0.5143  0.0602   0.6863\n",
              "Random Forest          0.9389     0.0421  0.1143  0.0615   0.6572"
            ]
          },
          "execution_count": 120,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Build the comparison table\n",
        "comparison_df = pd.DataFrame([lr_results, rf_results]).set_index(\"model\")\n",
        "comparison_df = comparison_df.round(4)\n",
        "comparison_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "766ff77a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "766ff77a",
        "outputId": "9bd80d37-35cc-490a-fcc4-29e5cfdc55ec"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1100x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualise the comparison across all metrics\n",
        "comparison_df.plot(kind=\"bar\", figsize=(11, 6), colormap=\"viridis\")\n",
        "plt.title(\"Model Comparison Across Evaluation Metrics\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.xticks(rotation=0)\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c45e881a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "c45e881a",
        "outputId": "9990b066-28f1-4851-9d03-6d00c9b0fbd1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "\n",
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              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Train ROC-AUC</th>\n",
              "      <th>Test ROC-AUC</th>\n",
              "      <th>Gap</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.7727</td>\n",
              "      <td>0.6863</td>\n",
              "      <td>0.0864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.9496</td>\n",
              "      <td>0.6572</td>\n",
              "      <td>0.2924</td>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "    }\n",
              "\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6d6e64e0-fae4-4101-85a9-cd7d4831aa28 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6d6e64e0-fae4-4101-85a9-cd7d4831aa28');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                 Model  Train ROC-AUC  Test ROC-AUC     Gap\n",
              "0  Logistic Regression         0.7727        0.6863  0.0864\n",
              "1        Random Forest         0.9496        0.6572  0.2924"
            ]
          },
          "execution_count": 122,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Compare train vs test performance to check for overfitting\n",
        "train_auc_lr = roc_auc_score(y_train, log_reg_tuned.predict_proba(X_train_scaled)[:, 1])\n",
        "test_auc_lr = lr_results[\"roc_auc\"]\n",
        "\n",
        "train_auc_rf = roc_auc_score(y_train, rf_tuned.predict_proba(X_train)[:, 1])\n",
        "test_auc_rf = rf_results[\"roc_auc\"]\n",
        "\n",
        "overfitting_check = pd.DataFrame({\n",
        "    \"Model\": [\"Logistic Regression\", \"Random Forest\"],\n",
        "    \"Train ROC-AUC\": [train_auc_lr, train_auc_rf],\n",
        "    \"Test ROC-AUC\": [test_auc_lr, test_auc_rf],\n",
        "})\n",
        "overfitting_check[\"Gap\"] = overfitting_check[\"Train ROC-AUC\"] - overfitting_check[\"Test ROC-AUC\"]\n",
        "overfitting_check.round(4)\n",
        "\n",
        "# A larger gap between train and test ROC-AUC indicates a greater degree\n",
        "# of overfitting to the training data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a610e6a9",
      "metadata": {
        "id": "a610e6a9"
      },
      "source": [
        "# Explainable AI\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dac73797",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 988
        },
        "id": "dac73797",
        "outputId": "388829de-51d4-4997-b16f-cc952dd0a33d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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9Vv/++6/Duv/++8/hda5cuVKFyAIFCjjsFxMTo6JFizoElFtFR0fLZrM53EN6sxw5bvxXq2TJkuratauWLFmiL7/8UjVq1FBwcLBat259T+dN1apVNWjQIFksFv3xxx+aN2+eLl++nOppklndm9OnT8vHx0d58uRx2O6xxx5L9b5pLZdufN23b9+u+Ph4h3OnRIkSDtvly5cvzZry5cunS5cupRo3LYUKFbrt94CMft+79byMi4vT5cuXtWLFCq1YsSLN90j5d9mzZ0/99NNPateunUqXLq169erp2WeftU9DBrISQQ8A8EDz8fFJ8+MXLly4IOlGwLgXVqtV/v7+Gj58eJrrU4JOTEyMXn75ZZUtW1bh4eF65JFH5OHhoR9++EFLly5N14NQbvfwmdtd/UzrCZtWq1X16tWzX5G8VXo+biItJpMpQ8tttzykwhky8oRRi8Wirl276t9//1WPHj1UtmxZ5c6dW+fPn1d4eHiqr8/tjiujrFar3NzctGjRojTHvDnAhIeHKyQkRFu3btWOHTs0fvx4LViwQJ999lmqQJ1eNweYBg0aqGzZsurdu7eWLVtm/zgBV/Ums279RYDk2vPwVreelyn9a926tUJCQtLcJ+U+Yj8/P23cuFHff/+9tm3bpm+++UYff/yx+vfvr4EDBzq3cGQ7BD0AgJ2vr6927typ//77z+G32ydOnLCvv9mff/6Zaozo6Gh5eXll2cMDypcvr127dslqtTr8B3D//v3y8vJK80pBRpQqVUqHDx9WnTp17vgU0G+//VaJiYmaN2+ewxWHm6d2prjdOCmfiXX58mWHz8e69UrW3eqNj4+/7dUJV7lw4UKqKzPR0dGS/u+88fX11ZEjR1J9LVPOr1uv5KTldr09evSooqOjNWXKFIcPC9+xY0dGD8WuVKlS2r59uy5dunTbq3qlSpWSzWbTo48+mq5zMeUpiv369dPu3bv14osv6pNPPtHgwYMzXefNGjVqpCeeeELz58/XCy+8oNy5czulN76+vvr555919epVh6t6J0+edNgu5Wt663Lpxte9UKFCqa4E328Z/b53K29vb/uDndLz7zJ37txq0aKFWrRoocTERA0YMEDz589X7969lStXrnQ9jRhID+7RAwDYPfnkk7JYLProo48cli9dulRubm568sknHZbv2bPHfu+JJJ09e1Zbt25VvXr1suyqQLNmzfT333/rm2++sS+Li4vTxo0b9dRTT6V5/15GNG/eXOfPn9dnn32Wat21a9cUHx8v6f+uKNx8BeHKlStavXp1qv28vLxSTW2UboQCSQ73osXHx2vdunUZqnfPnj32R7rf7PLly0pOTk73WFkpOTnZYdpaYmKiVqxYIW9vb1WqVEnSjfPrr7/+0oYNGxz2W758uXLnzp2upyd6eXlJSj11NCU43vz1sdlsqZ4OmhFNmjSRzWbT3LlzU61LeZ8mTZrIZDJp7ty5qa4u2Ww2/fPPP5JuTI+89Wvj7+8vd3d3h6nJ9/rxCpLUo0cPXbp0yX5OO6M3Tz75pJKTkx0+QsNisejDDz902K5o0aKqUKGC1q1b5/A1O3r0qHbs2KGGDRtmuoasktHve7cymUxq2rSpNm3apKNHj6Zaf/N06pTzIUXOnDnl5+cnm82mpKQkSf93jjvjvl9kL1zRAwDYBQcHq1atWpo5c6ZOnz4ts9msHTt2aOvWrXrppZfsQSWFv7+/unfv7vDxCtKNB0rczbfffqvDhw9LkpKSknTkyBH7RxkEBwerfPnykqSmTZsqMDBQw4cP17Fjx1SoUCF98sknslgs6Xqfu2nTpo2+/vprvfnmm4qKilK1atVksVh04sQJbdy4UYsXL1ZAQIDq1asnDw8P9enTRx06dNDVq1e1cuVKFS5cONXU0kqVKumTTz7Ru+++q9KlS8vb21t16tRRvXr1VKJECY0cOVInTpyQyWTS6tWrVahQoXRf1evevbu+/fZb9enTRyEhIapUqZISEhJ09OhRbdq0SVu3bnXJo9iLFi2qRYsW6fTp0ypTpow2bNigQ4cOady4cfb7xV544QWtWLFC4eHh+t///idfX19t2rRJu3fv1ogRI1LdI5WWUqVKKX/+/Pr000+VJ08e5c6dW1WqVFHZsmVVqlQpTZkyRefPn1fevHm1adOmNAN3etWuXVtt2rTR8uXL9eeff6pBgwayWq3atWuXatWqpc6dO6tUqVIaNGiQZsyYodOnT+uZZ55Rnjx5FBsbqy1btqh9+/bq3r27fv75Z7311ltq1qyZypQpI4vFos8//9weElLc68crSFLDhg3l7++vpUuXqlOnTk7pTXBwsKpVq2Y/7nLlyumbb75JM5wMHTpUPXv21AsvvKDQ0FD7xyvky5cvzQfd3G8Z/b6XliFDhigqKkrt27dXu3btVK5cOf3777/63//+p507d+qXX36RdOPfb5EiRVStWjUVLlxYJ06c0IcffqiGDRvaz/+UX4zMnDlTLVq0kIeHh5566imXX/nEw4egBwCwc3d317x58zRnzhxt2LBBa9aska+vr4YOHapu3bql2r5mzZoKDAzUO++8ozNnzqhcuXKaNGmSPaTdyTfffOPwQdkHDx60P/2vePHi9jFMJpMWLlyoqVOnavny5bp+/boCAgI0adIklS1bNkuO+Z133tHSpUv1+eefa/PmzfLy8tKjjz6qsLAw+3S8smXLas6cOZo1a5amTJmiIkWK6MUXX5S3t3eqz9nq37+/zpw5o8WLF+vq1at64oknVKdOHXl4eGju3LkaO3asZs+eLR8fH7300kvKnz//be8RvJWXl5eWL1+uBQsWaOPGjVq3bp3y5s2rMmXKaMCAAVn+QJj0KlCggCZPnqzx48frs88+U5EiRTRmzBi1b9/evo2np6eWL1+u6dOna+3atfrvv//02GOPadKkSWrbtm263sfDw0OTJ0/W22+/rYiICCUnJ9v3nz9/vv2+t1y5cqlx48bq1KmT2rRpk+njmjRpksxms1atWqWpU6cqX758qly5soKCguzb9OrVS2XKlNHSpUv1zjvvSLpxDterV0/BwcGSbkzZrF+/vr777judP39eXl5eMpvNWrRokQIDAzNd3+1069ZN4eHh+vLLL53Sm5TvFRMnTtQXX3whNzc3BQcHKzw83GF6qCTVrVtXixcv1pw5czRnzhzlyJFDNWvW1BtvvKGSJUtmwdHem4x+30tLkSJFtHLlSr3zzjvavHmzPvnkExUsWFDlypXT66+/bt/uhRde0JdffqklS5YoPj5exYsXV1hYmPr162ffpkqVKnr11Vf16aefatu2bbJardq6dStBDxnmZnPFXawAgIee2WxWp06dNGbMGFeXAhcLCwvTP//8o/Xr17u6FADA/8c9egAAAABgMAQ9AAAAADAYgh4AAAAAGAz36AEAAACAwXBFDwAAAAAMhqAHAAAAAAbD5+gBBma1WpWcnCx3d3e5ubm5uhwAAADcA5vNJqvVqhw5csjd/c7X7Ah6gIElJyfrwIEDri4DAAAAWSggIEA5c+a84zYEPcDAUn7TU7Fixbt+M0DGWCwWHThwQAEBATKZTK4ux1DorXPRX+eht85Db52L/jpPVvc2Zby7Xc2TCHqAoaVM1zSZTHzjdhJ66zz01rnor/PQW+eht85Ff50nq3ubnltyeBgLAAAAABgMQQ8AAAAADIagBwAAAAAGQ9ADAAAAAIMh6AEAAACAwRD0AAAAAMBgCHoAAAAAYDAEPQAAAAAwGIIeAAAAABgMQQ8AMsnLy8vVJRgWvXUu+us89NZ56K1z0V/jyeHqAgA4n8lkcnUJhmMymVSxYkVXl2FI9Na56K/z0FvnobfORX8zx2K1yuT+4F43I+gB2UDEpgU6+neMq8sAAAAwhLKFfTWp5QBXl3FHBD0gG4iOO6PDF6JdXQYAAADukwf3WiMAAAAAIFMIegAAAABgMAQ9AAAAADAYgh4AAAAAGAxBD04VFhamCRMmuLoMAAAAIFsh6MGpIiMj9eqrr6Zr29jYWJnNZh06dMjJVaWP2WzWli1bnP4+D9pxAwAA4OHHxyvAqQoWLOiS901KSpKHh4dL3jtFYmKicubM6dIaAAAAkD1xRQ9OdfPUzeDgYM2fP1/Dhw9XUFCQGjVqpBUrVti3ffrppyVJzz33nMxms8LCwuzrVq5cqebNmysgIEDNmjXTRx99ZF+XckVsw4YN6ty5swICAvTll1/edb/ExES99dZbql+/vgICAvTUU09pwYIF9lolqX///jKbzfbXdxIZGak2bdpo5cqVCg4OVpUqVSRJP/74o1588UXVqFFDtWrVUu/evRUT838fXp7Z4wYAAABuhyt6uK+WLFmigQMHqk+fPtq0aZMiIiJUs2ZNlS1bVitXrlS7du20dOlSlStXzn5F7osvvtDs2bM1ZswYVahQQYcOHdLo0aOVO3duhYSE2MeePn26wsPDVaFCBeXKleuu+y1fvlzffvutZs2apUceeURnz57VuXPnJEmrVq1SnTp1NGnSJDVo0EAmkyldxxcTE6NNmzZp7ty5cne/8XuUhIQEde3aVWazWfHx8Zo9e7b69++vzz//XO7u7vd83AAAAMCtCHq4r5588kl16tRJktSzZ08tXbpUUVFRKlu2rLy9vSXdmO7p4+Nj3ycyMlLh4eFq0qSJJKlkyZI6duyYVqxY4RB4XnrpJfs26dnv7NmzKl26tKpXry43Nzf5+vra902pJX/+/A613E1SUpKmTp1q31+SmjZt6rDNxIkTVadOHR07dkz+/v73fNwAAABwDYvFkq71d9suq97vZgQ93Fdms9n+dzc3NxUpUkQXL1687fbx8fGKiYnRyJEjNXr0aPvy5ORk5cuXz2HbypUrZ2i/kJAQdevWTc2aNVODBg3UqFEj1a9f/56Or0SJEg4hT5Kio6M1Z84c7du3T//8849sNpsk6ezZs/L397/n4wYAAIBrHDlyRAkJCXfd7sCBA/ehGkcEPdxXOXI4nnJubm724JOW+Ph4SdK4ceNUtWpVh3UpUyNT5M6dO0P7VapUSVu3btWPP/6on376SYMGDVLdunU1Z86cDB7V//Hy8kq1rE+fPvL19dX48eNVtGhRWa1WPfvss0pKSrrtOBk5bgAAALjGzRcx0mKxWHTgwAEFBASk+1ag9IyXHgQ9PDBS7k27+ZJ0kSJFVLRoUZ06dUqtW7dO91jp3S9v3rxq0aKFWrRooaZNm6pHjx66dOmSChYsKA8Pj3u+zP7PP//o5MmTGj9+vGrUqCFJ+u233xy2ycrjBgAAwP2T3vBmMpmyJOhlBEEPD4zChQvL09NT27ZtU/HixZUrVy7ly5dPAwcO1Pjx45UvXz41aNBAiYmJ+v3333X58mV17dr1tuPdbb8lS5bIx8dHFSpUkLu7uzZu3CgfHx/lz59fkuTr66udO3eqWrVqypkzpwoUKJDhYypQoIAKFiyoFStWyMfHR2fOnNGMGTOcetwAAAAAQQ8PjBw5cmjUqFF65513NGfOHNWoUUPLly9Xu3bt5Onpqffee09Tp05V7ty55e/vr5deeumO491tvzx58mjx4sX6888/5e7uroCAAC1cuNA+NXLYsGGaPHmyVq5cqWLFiunbb7/N8DG5u7tr5syZGj9+vJ599lk99thjGjVqlMNHKGT1cQMAAAButjvdIAXgoWaxWLR3717NOrRW+88dc3U5AAAAhlC+aBmt6DL5rtul/F8sMDAwy+7RS+94PNUBAAAAAAyGqZtAOrVs2VJnzpxJc93YsWN5aAoAAAAeGAQ9IJ0WLlyo5OTkNNcVLlz4PlcDAAAA3B5BD0gnX19fV5cAAAAApAv36AEAAACAwXBFD8gGyniXUKI17WmnAAAAyJiyhR/8mV4EPSAbiGjaO0se6QsAAIAbLFarTO4P7gTJB7cyAFnGYrG4ugTDsVgsOnjwIL11AnrrXPTXeeit89Bb56K/mfMghzyJoAcAmZaQkODqEgyL3joX/XUeeus89Na56K/xEPQAAAAAwGAIegAAAABgMAQ9AAAAADAYgh4AAAAAGAxBDwAAAAAMhqAHAAAAAAZD0AMAAAAAgyHoAQAAAIDBEPQAAAAAwGAIegAAAABgMAQ9AAAAADAYgh4AAAAAGAxBDwAAAAAMhqAHAAAAAAZD0AMAAAAAgyHoAQAAAIDBEPQAAAAAwGAIegAAAABgMAQ9AAAAADAYgh4AAAAAGAxBDwAAAAAMhqAHAJnk5eXl6hIMi94CAHBvcri6AADOZzKZXF2C4ZhMJlWsWNHVZRjSw9pbi9Uqkzu/PwUAPBgIekA2ELFpgY7+HePqMgDDKlvYV5NaDnB1GQAA2BH0gGwgOu6MDl+IdnUZAAAAuE+YYwIAAAAABkPQAwAAAACDIegBAAAAgMEQ9AAAAADAYAh6AAAAAGAwBD3cVlhYmCZMmJCubdesWaMaNWo4uaKsYTabtWXLFleXAQAAADgNQQ/3TWRkpNq0aZOhfZwRyrZv364nn3wyS8cEAAAAHiR8jh6yHR8fH1eXAAAAADgVV/QgSYqPj9fQoUMVFBSk+vXr6/3333dYn5iYqClTpqhBgwYKDAxUu3btFBUVlWqcLVu2qEmTJgoICFD37t119uxZSTemds6dO1eHDx+W2WyW2WzWmjVr7lhTcHCwJKl///4ym83215L08ccf65lnnlHlypXVtGlTrVu3Lt3HevNVwtjYWJnNZn3zzTcKCwtT1apV1bp1a+3Zs8dhn127dtnX16xZU927d9e///5r78348eNVp04dBQQE6MUXX9T+/fvt+0ZFRclsNmvbtm167rnnVKVKFXXp0kUXL17UDz/8oObNm6tatWoaMmSIEhIS7PtZrVYtWLBAwcHBqlKlilq3bq2NGzem+zgBAACQfXFFD5KkqVOn6tdff9W7774rb29vzZw5U//73/9Uvnx5SdJbb72lY8eOaebMmSpatKg2b96sHj166Msvv1SZMmUkSdeuXdO8efM0ZcoUeXh4aOzYsRo8eLA+/fRTtWjRQn/88Ye2bdumJUuWSJLy5ct3x5pWrVqlOnXqaNKkSWrQoIFMJpMkafPmzZo4caKGDx+uunXr6vvvv9eIESNUvHhx1a5dO1PHP3PmTA0bNkylS5fWzJkzNWTIEH3zzTfKkSOHDh06pJdfflnPP/+8Ro4cKZPJpKioKFksFnvvNm3apMmTJ8vX11eLFy9Wjx499M0336hgwYL295g7d65Gjx4tLy8vDRo0SIMGDVLOnDk1Y8YMxcfHq3///lq+fLl69eolSVqwYIG++OILjR07VmXKlNGvv/6qN954Q97e3nriiScydZwAnCvl+8KDLKXGh6HWhw29dR5661z013myurcZGYegB129elWrVq3StGnTVKdOHUnS5MmT1bBhQ0nSmTNntGbNGn333XcqVqyYJKl79+7atm2b1qxZo9dee02SlJSUpDFjxqhq1ar2MVq0aKH9+/erSpUqyp07t0wmU7qnTnp7e0uS8ufP77DPe++9p5CQEHXq1EmS9Nhjj2nv3r16//33Mx30unXrpkaNGkmSBg4cqJYtW+rPP/+Un5+fFi9erMqVKysiIsK+/eOPPy7pxpXQTz/9VJMmTbL3a9y4cdqxY4dWrVqlHj162PcZNGiQqlevLkkKDQ3VjBkztGXLFpUsWVKS1LRpU0VFRalXr15KTEzUggULtGTJEgUFBUmSSpYsqV27dmnFihUEPeABdeTIEYcr8w+yAwcOuLoEw6K3zkNvnYv+Oo8rekvQg06dOqWkpCR7QJOkggUL6rHHHpMkHT16VBaLRc2aNXPYLzEx0eGKVY4cORQQEGB/7efnp/z58+v48eOqUqVKltV74sQJvfDCCw7LqlWrpmXLlmV6TLPZbP97SqiMi4uTn5+fDh06lOrYU8TExCgpKUnVqlWzL/Pw8FCVKlV0/Pjx275H4cKF5eXlZQ95klSkSBH7N4E///xTCQkJ6tatm8MYSUlJqlChQiaPEoCz3fzv/EFlsVh04MABBQQE2GdKIGvQW+eht85Ff50nq3ubMl56EPRwV/Hx8TKZTFq9enWqEzR37twuqipreXh42P/u5uYm6cY9cpLk6emZJe+RI8f//XNzc3NzeJ2yLOU94+PjJd2YvplyFTVFzpw5s6QeAFnvYfoPkslkeqjqfZjQW+eht85Ff53HFb3lYSxQyZIl5eHhoX379tmX/fvvv4qOjpYkVahQQRaLRXFxcSpdurTDn5unVCYnJ+v333+3vz5x4oQuX74sPz8/STfCVEqQSS8PD49Uc5HLli2r3bt3OyzbvXu3ypUrl6Gx08tsNmvnzp1pritVqpQ8PDwc6klKStKBAwfuqR4/Pz/lzJlTZ86cSdXzRx55JNPjAgAAIHvgih6UJ08ePf/885o2bZoKFiyowoULa+bMmfYrW4899phatWqloUOHKjw8XBUqVNA///yjnTt3ymw22+9t8/Dw0Lhx4zRq1CiZTCaNGzdOgYGB9mmbvr6+io2N1aFDh1SsWDHlzZv3rlenfH19tXPnTlWrVk05c+ZUgQIF1KNHDw0aNEgVKlRQ3bp19d1332nz5s32h7xktV69eqlVq1aKiIhQhw4d5OHhoaioKDVr1kze3t568cUXNXXqVBUoUEAlSpTQ4sWLde3aNYWGhmb6PfPmzatu3bpp0qRJstlsql69uq5cuaLdu3crb968CgkJycIjBAAAgNEQ9CBJGjp0qOLj49W3b1/lyZNHXbt21X///WdfP2nSJM2bN0+TJ0/WhQsXVLBgQQUGBtpDnnRjimPPnj01ZMgQnT9/XjVq1NCECRPs65s2barNmzerS5cuunz5siZNmqS2bdvesa5hw4Zp8uTJWrlypYoVK6Zvv/1WzzzzjEaMGKH3339fEydOlK+vryZOnKhatWpleV+kG0H3/fff19tvv6127drJ09NTVapU0bPPPitJev3112Wz2TR06FBdvXpVlStX1uLFi1WgQIF7et9BgwbJ29tbCxYsUGxsrPLly6eKFSuqT58+WXFYAAAAMDA3m81mc3URAJzDYrFo7969mnVorfafO+bqcgDDKl+0jFZ0mezqMtIl5ftCYGAg9+JkMXrrPPTWueiv82R1bzMyHvfoAQAAAIDBMHUTLvPFF1/ozTffTHNdiRIl9NVXXz0QYwIAAAAPG4IeXCY4ONjhs/tudutHD7hyTAAAAOBhw/984TJ58+ZV3rx5H/gxAQAAgIcN9+gBAAAAgMFwRQ/IBsp4l1CiNdnVZQCGVbawr6tLAADAAUEPyAYimvbmccmAk1msVpncmSgDAHgw8BMJyAYsFourSzAci8WigwcP0lsneFh7S8gDADxI+KkEAJmUkJDg6hIMi94CAHBvCHoAAAAAYDAEPQAAAAAwGIIeAAAAABgMQQ8AAAAADIagBwAAAAAGQ9ADAAAAAIMh6AEAAACAwRD0AAAAAMBgCHoAAAAAYDAEPQAAAAAwGIIeAAAAABgMQQ8AAAAADIagBwAAAAAGQ9ADAAAAAIMh6AEAAACAwRD0AAAAAMBgCHoAAAAAYDAEPQAAAAAwGIIeAAAAABgMQQ8AAAAADIagBwAAAAAGQ9ADgEzy8vJydQmGRW8BALg3OVxdAADnM5lMri7BcEwmkypWrOjqMgwpq3prsVplcuf3mQCA7ImgB2QDEZsW6OjfMa4uA7hvyhb21aSWA1xdBgAALkPQA7KB6LgzOnwh2tVlAAAA4D5hTgsAAAAAGAxBDwAAAAAMhqAHAAAAAAZD0AMAAAAAgyHo4YEQFhamCRMmuLSG4OBgLV261P7abDZry5YtrisIAAAAyCSCHh4IkZGRevXVV7NkLKMFtDVr1qhGjRquLgMAAAAPET5eAQ+EggULuroEAAAAwDC4oocHws1TN4ODgzV//nwNHz5cQUFBatSokVasWGHfNjExUW+99Zbq16+vgIAAPfXUU1qwYIF9X0nq37+/zGaz/XVMTIz69u2runXrKigoSM8//7x++umndNcXGxsrs9msDRs2qGPHjqpSpYqef/55nTx5Uvv371fbtm0VFBSkHj16KC4uzmHflStXqnnz5goICFCzZs300UcfpRr3m2++UVhYmKpWrarWrVtrz549kqSoqCgNHz5cV65ckdlsltlsVmRkZCY6DAAAgOyEK3p4IC1ZskQDBw5Unz59tGnTJkVERKhmzZoqW7asli9frm+//VazZs3SI488orNnz+rcuXOSpFWrVqlOnTqaNGmSGjRoIJPJJEmKj49Xw4YNNXjwYOXMmVPr1q1Tnz59tHHjRpUoUSLddUVGRmrEiBEqUaKERowYoSFDhihPnjwaOXKkvLy8NGjQIM2ePVtjx46VJH3xxReaPXu2xowZowoVKujQoUMaPXq0cufOrZCQEPu4M2fO1LBhw1S6dGnNnDlTQ4YM0TfffKOgoCCNGDFCc+bM0caNGyVJuXPnzqo2A4ZnsVhcXcIDJ6Un9Cbr0VvnobfORX+dJ6t7m5FxCHp4ID355JPq1KmTJKlnz55aunSpoqKiVLZsWZ09e1alS5dW9erV5ebmJl9fX/t+3t7ekqT8+fPLx8fHvrx8+fIqX768/fWgQYO0ZcsWffvtt+rcuXO66+rWrZsaNGggSerSpYtee+01LV26VNWrV5ckhYaGas2aNfbtIyMjFR4eriZNmkiSSpYsqWPHjmnFihUOQa9bt25q1KiRJGngwIFq2bKl/vzzT/n5+Slfvnxyc3NzOB4A6XPkyBElJCS4uowH0oEDB1xdgmHRW+eht85Ff53HFb0l6OGBZDab7X93c3NTkSJFdPHiRUlSSEiIunXrpmbNmqlBgwZq1KiR6tevf8fxrl69qrlz5+r777/XX3/9JYvFomvXrunMmTOZrqtw4cJpLkuZuhkfH6+YmBiNHDlSo0ePtm+TnJysfPny3XbclEAXFxcnPz+/DNUHwNHN/7Zwg8Vi0YEDBxQQEGCf9YCsQW+dh946F/11nqzubcp46UHQwwMpRw7HU9PNzU02m02SVKlSJW3dulU//vijfvrpJw0aNEh169bVnDlzbjvelClT9NNPP2nYsGEqVaqUPD09NXDgQCUlJWWoLg8PD4eabq3Vzc1NVqtV0o2gJ0njxo1T1apVHcZxd3e8PTatcVPGAZB5/Ifl9kwmE/1xEnrrPPTWueiv87iitwQ9PJTy5s2rFi1aqEWLFmratKl69OihS5cuqWDBgvLw8Eg1f3nPnj0KCQlR48aNJd24wnf69Gmn1likSBEVLVpUp06dUuvWrTM9TlrHAwAAANwJQQ8PnSVLlsjHx0cVKlSQu7u7Nm7cKB8fH+XPn1+S5Ovrq507d6patWrKmTOnChQooNKlS2vz5s0KDg6Wm5ubZs2adV+umA0cOFDjx49Xvnz51KBBAyUmJur333/X5cuX1bVr13SN4evrq/j4eO3cuVNms1leXl7y8vJycuUAAAB4mBH08NDJkyePFi9erD///FPu7u4KCAjQwoUL7dMhhw0bpsmTJ2vlypUqVqyYvv32W4WHh2vEiBHq0KGDChUqpJ49e+rq1atOr7Vdu3by9PTUe++9p6lTpyp37tzy9/fXSy+9lO4xqlWrpg4dOmjQoEG6dOmSXnnlFQ0YMMCJVQMAAOBh52ZLufEJgOFYLBbt3btXsw6t1f5zx1xdDnDflC9aRiu6THZ1GQ+klO8LgYGB3IuTxeit89Bb56K/zpPVvc3IeHxgOgAAAAAYDEEPAAAAAAyGoAcAAAAABkPQAwAAAACDIegBAAAAgMHw8QpANlDGu4QSrcmuLgO4b8oW9nV1CQAAuBRBD8gGIpr25nHJyHYsVqtM7kxcAQBkT/wEBLIBi8Xi6hIMx2Kx6ODBg/TWCbKqt4Q8AEB2xk9BAMikhIQEV5dgWPQWAIB7Q9ADAAAAAIMh6AEAAACAwRD0AAAAAMBgCHoAAAAAYDAEPQAAAAAwGIIeAAAAABgMQQ8AAAAADIagBwAAAAAGQ9ADAAAAAIMh6AEAAACAwRD0AAAAAMBgCHoAAAAAYDAEPQAAAAAwGIIeAAAAABgMQQ8AAAAADIagBwAAAAAGQ9ADAAAAAIMh6AEAAACAwRD0AAAAAMBgCHoAAAAAYDAEPQAAAAAwGIIeAGSSl5eXq0swLHoLAMC9yeHqAgA4n8lkcnUJhmMymVSxYkVXl2FIWdFbi9Uqkzu/ywQAZF8EPSAbiNi0QEf/jnF1GcB9Ubawrya1HODqMgAAcCmCHpANRMed0eEL0a4uAwAAAPcJ81oAAAAAwGAIegAAAABgMAQ9AAAAADAYgh4AAAAAGAxBDwAAAAAM5qENepGRkWrTpk22ed/bCQsL04QJE1xdxn1hNpu1ZcsWSVJsbKzMZrMOHTqU6fFc1bsH7RwCAACA8TwUQe/m/+C7Wrdu3bR06VJXl+EUWRGe7pdHHnlE27dv1+OPPy5JioqKktls1uXLl9M9RmRkpF599VVnlXhbRj6HAAAA8GDgc/QyKE+ePMqTJ4+ry3hoJSUlycPD457HMZlM8vHxuacxChYseM91ZMbdzqHExETlzJnzPlYEAAAAo8nQFb2wsDCNGzdOEyZMUM2aNVW3bl199tlnio+P1/DhwxUUFKTGjRvrhx9+sO/zyy+/KDQ0VJUrV1b9+vU1ffp0JScnO4w5fvx4TZ06VU888YTq1aunyMhI+/rg4GBJUv/+/WU2m+2vU6xbt07BwcGqXr26Bg8erP/++8++buPGjWrVqpWqVKmiWrVq6eWXX1Z8fPxdjzMqKkqhoaEKDAxUjRo11KFDB50+fVpS6ml34eHh6tevn9577z3Vr19ftWrV0tixY5WUlGTfJjExUdOmTVPDhg1VuXJlNW7cWCtXrrSvP3r0qHr06KGgoCDVrVtXb7zxhuLi4u5aZ1rWrVuntm3bKigoSPXq1dOQIUN08eJF+/p///1XQ4YMUe3atVWlShU1adJEq1evliQ9/fTTkqTnnntOZrNZYWFh6XrPVatWqWXLlvav8VtvvWVfZzab9fHHH6tPnz4KDAzU/PnzJUlbtmxRSEiIAgIC9PTTT2vu3LkO50V0dLQ6deqkgIAAtWjRQjt27HB4z5uvPsbGxqpLly6SpJo1a8psNis8PPyudd86dTM4OFjz58+3n8uNGjXSihUrHPbZv3+/nnvuOQUEBKht27bavHmzw1XQNWvWqEaNGg77bNmyRWaz2f76dufQvHnzVL9+fTVr1kxz587Vs88+m6rmNm3aaNasWXc9NgAAAGRvGb6it3btWvXo0UMrV67Uhg0bFBERoc2bN6tx48bq3bu3li5dqqFDh+r777/X5cuX1atXL4WEhGjKlCk6efKkRo0apVy5cmnAgAEOY3bt2lWfffaZ9u7dq/DwcFWrVk316tXTqlWrVKdOHU2aNEkNGjSQyWSy7xcTE6OtW7dq/vz5unz5sgYNGqRFixZp8ODBunDhgoYMGaI33nhDzzzzjK5evarffvtNNpvtjseXnJys/v37q127dnr77beVlJSk/fv3y83N7bb7REVFycfHRx988IFiYmI0ePBgVahQQe3bt5ckDR06VHv37tWoUaNUvnx5xcbG6p9//pEkXb58WS+99JLatWun4cOH6/r165o+fboGDRqkZcuWZfTLo+TkZL366qsqW7asLl68qMmTJys8PFyLFi2SJM2ePVvHjx/XokWLVKhQIcXExOjatWuSpJUrV6pdu3ZaunSpypUrl64rbx9//LEmT56sIUOG6Mknn9SVK1e0e/duh23mzp2rIUOGaOTIkTKZTPrtt980bNgwjRo1SjVq1FBMTIxGjx4tSXrllVdktVo1YMAAFS5cWCtXrtSVK1c0ceLE29bwyCOPKDIyUgMGDNDGjRuVN29eeXp6Zrh3krRkyRINHDhQffr00aZNmxQREaGaNWuqbNmyunr1qnr37q26detq2rRpio2NzbJ7/Hbu3Km8efNqyZIlkqR8+fLpnXfe0f79+1WlShVJ0sGDB3XkyBHNnTs3S94TMDqLxeLqEh5IKX2hP1mP3joPvXUu+us8Wd3bjIyT4aBXvnx59evXT5LUu3dve2BICTX9+/fXJ598oiNHjui7775T8eLFNWbMGLm5ucnPz0/nz5/X9OnT1b9/f7m737igaDab9corr0iSypQpow8//FA7d+5UvXr15O3tLUnKnz9/qql6NptNkyZNUt68eSVJrVu31s6dOzV48GD99ddfSk5OVuPGjeXr62t/n7v577//dOXKFT311FMqVaqUJMnPz++O+xQoUEBjxoyRyWSSn5+fGjZsqJ07d6p9+/Y6efKkvv76ay1ZskR169aVJJUsWdK+74cffqiKFSvqtddesy+bOHGiGjZsqJMnT+qxxx67a803Cw0Ntf+9ZMmSGjlypEJDQ3X16lXlyZNHZ86cUYUKFRQQECBJevTRR+3bp/S6YMGC6Z4WOW/ePHXt2lUvvfSSfVlKMEnx7LPP6vnnn7e/HjFihP0XACl1vvrqq5o2bZpeeeUV/fTTTzpx4oQWL16sYsWKSZIGDx6snj17plmDyWRSgQIFJEmFCxdW/vz501V7Wp588kl16tRJktSzZ08tXbpUUVFRKlu2rNavXy+r1aqJEycqV65cevzxx3Xu3DlFRERk+v1S5M6dW+PHj3eYslm/fn2tWbPG3s81a9aoZs2aDucPgNs7cuSIEhISXF3GA+vAgQOuLsGw6K3z0Fvnor/O44reZjjo3RyWTCaTChYsKH9/f/uyIkWKSJIuXryo48ePKygoyOFqWPXq1RUfH69z586pRIkSqcaUJB8fH4fphrfj6+trD3mSVLRoUft+5cuXV506ddSqVSvVr19f9evXV9OmTe2B4HYKFiyotm3bqnv37qpXr57q1Kmj5s2bq2jRorfdp1y5cg5XGn18fHT06FFJ0qFDh2QymVSzZs009z18+LCioqIUFBSUal1MTEyGg97vv/+uuXPn6vDhw/r333/tVzDPnj2rcuXK6cUXX9TAgQN18OBB1atXT88884yqVauWofdIcfHiRV24cEF16tS543aVK1d2eH348GHt3r3bPo1TuvHbievXryshIUHHjx9X8eLF7SFPUpr9cYabz0U3NzcVKVLEfk4dP35cZrNZuXLlyvK6/P39U92X1759e40YMULDhw+Xm5ubvvzySw0fPjxL3g/IDtLzy73syGKx6MCBAwoICHD42YV7R2+dh946F/11nqzubcp46ZHhoJcjh+Mubm5uDstSQt3dpkjebcz07H/rfje/r8lk0pIlS7R7927t2LFDy5cv18yZM/XZZ5/d9YrIpEmTFBYWpm3btunrr7/WrFmztGTJEgUGBma4/rtNIYyPj9dTTz2l119/PdW6jD5sJD4+Xt27d7ffC1moUCGdPXtW3bt3t98z2LBhQ3333Xf64YcftGPHDr388svq1KmThg0blqH3kuQQeO4kd+7cqeocMGCAmjRpkukxnSWz52IKd3f3VNvffL/m7Xh5eaVa9tRTTylnzpzavHmzPDw8lJycrGbNmqW7FiC74z8rd2YymeiRk9Bb56G3zkV/nccVvXXqxyv4+flpz549Dv/x3bVrl/LkyaPixYunexwPD49MzWt1c3NT9erVNXDgQK1bt04eHh7p/piGihUrqnfv3vr000/l7++v9evXZ/j9pRtXaqxWq3799dc011eqVEl//PGHfH19Vbp0aYc/twakuzlx4oQuXbqk119/XTVq1JCfn1+aV0a9vb0VEhKi6dOna8SIEfYHjqTck5feXufNm1e+vr7auXNnhuqsWLGiTp48mep4S5cuLXd3d/n5+encuXO6cOGCfZ+9e/feccyM1p4Zfn5+OnLkiK5fv37bugoVKqSrV686PPTn8OHDmXq/HDly6LnnntOaNWu0Zs0atWzZMtP3HgIAACB7cWrQ69ixo86dO6dx48bp+PHj2rJliyIjI9W1a1f7/XnpkRIm/vrrL/3777/p2mffvn2aP3++Dhw4oDNnzuibb75RXFycypYte8f9Tp06pRkzZmjPnj06ffq0tm/frujo6LvudzuPPvqoQkJCNGLECG3ZskWnTp1SVFSUNmzYIOlGj/7991+99tpr2r9/v2JiYrRt2zYNHz48w6GlRIkS8vDw0PLly3Xq1Clt3bpV7777rsM2s2fP1pYtW/Tnn3/qjz/+0Pfff2+/B7Fw4cLy9PTUtm3b9Pfff+vKlSt3fc8BAwZoyZIlWrZsmaKjo/W///1Py5cvv+M+/fv31+eff665c+fqjz/+0PHjx/XVV19p5syZkqS6deuqTJkyCg8P1+HDh/Xbb7/Z192Or6+v3Nzc9P333ysuLk5Xr169a+0Z9eyzz8rNzU2jRo3SsWPH9MMPP+j999932KZq1ary8vLS22+/rZiYGH355Zdas2ZNpt+zXbt2+vnnn7Vt2zaH+xwBAACAO3Fq0CtWrJgWLlyo/fv3q02bNoqIiFBoaKj69u2boXGGDRumn376SY0aNbI/wONu8ubNq19//VW9evVS06ZNNWvWLIWHh6thw4Z33M/Ly0snTpzQgAED1LRpU40ZM0adOnVShw4dMlTzzSIiItS0aVNFRESoefPmGj16tP0BAcWKFdMnn3wiq9Wq7t27q1WrVpo4caLy5cuXoTAs3bhSN3nyZG3cuFEtWrTQokWLUk3J9PDw0Ntvv63WrVurc+fOcnd319tvvy3pxhWkUaNGacWKFWrQoIH9oTt3khJiP/74Yz377LPq3bu3/vzzzzvu06BBA82fP1/bt29XaGio2rdvr6VLl9ofmuPu7q65c+fq2rVrCg0N1ciRIzV48OA7jlmsWDENGDBAM2bMUN26dTVu3Li71p5RefLk0fz583X06FE999xzmjlzZqoptwULFtS0adP0448/qlWrVvrqq68cnjCbUWXKlFFQUJDKli2rqlWr3ushAAAAIJtws2XkBiQADmJjY/X0009r3bp1qlChQpaPb7PZ1KRJE3Xs2FFdu3bN8P4Wi0V79+7VrENrtf/csSyvD3gQlS9aRiu6THZ1GQ+slO8LgYGB3IuTxeit89Bb56K/zpPVvc3IeBl+GAuA+yMuLk5fffWV/v77b7Vt29bV5QAAAOAhki2D3p0eib9o0SLVqFHjPlZze2fOnFHLli1vu/6rr76yf0SFszwsvbrVg9C7e1WnTh0VKlRIb7311l0/FgQAAAC4WbYMeuvWrbvtups/u83VihYtesda7/TZflnlYenVre5X7x599FEdOXIkS8a6lbPGBQAAgPFly6BXunRpV5eQLjly5HB5ra5+/8x6EHoHAAAAuEq2DHpAdlPGu4QSrcmuLgO4L8oW9nV1CQAAuBxBD8gGIpr25ilayFYsVqtMGfyIGgAAjISfgkA2YLFYXF2C4VgsFh08eJDeOkFW9JaQBwDI7vhJCACZlJCQ4OoSDIveAgBwbwh6AAAAAGAwBD0AAAAAMBiCHgAAAAAYDEEPAAAAAAyGoAcAAAAABkPQAwAAAACDIegBAAAAgMEQ9AAAAADAYAh6AAAAAGAwBD0AAAAAMBiCHgAAAAAYDEEPAAAAAAyGoAcAAAAABkPQAwAAAACDIegBAAAAgMEQ9AAAAADAYAh6AAAAAGAwBD0AAAAAMBiCHgAAAAAYDEEPAAAAAAyGoAcAAAAABkPQAwAAAACDIegBQCZ5eXm5ugQAAIA05XB1AQCcz2QyuboEwzGZTKpYsaKry3ioWKxWmdz5/SIAAPcDQQ/IBiI2LdDRv2NcXQaysbKFfTWp5QBXlwEAQLZB0AOygei4Mzp8IdrVZQAAAOA+YQ4NAAAAABgMQQ8AAAAADIagBwAAAAAGQ9ADAAAAAIMh6MEuPDxc/fr1c3UZDmJjY2U2m3Xo0CFJUlRUlMxmsy5fvnzfa7m1P2FhYZowYYL9dXBwsJYuXeq097+1FwAAAMDtEPQecLeGCWft87AICgrS9u3blS9fPknSmjVrVKNGjSx9j9sFqpEjR2ry5Mm33W/VqlV64YUXsrQWAAAAIDP4eAXcF0lJSfLw8LjncXLmzCkfH58sqCjjUsLl7Xh7e9+nSgAAAIA744reAyw8PFy//PKLli1bJrPZLLPZrNjYWP3yyy8KDQ1V5cqVVb9+fU2fPl3Jycl33MdisWjEiBEKDg5WlSpV1LRpU33wwQeZrs1qtWrRokVq3LixKleurEaNGmnevHmS/u+K2IYNG9S5c2cFBAToyy+/lCStXLlSzZs3V0BAgJo1a6aPPvrIYdz9+/frueeeU0BAgNq2bZvqqtrNUzejoqI0fPhwXblyxX6skZGRd63dbDZry5YtDstq1KihNWvWSJKefvppSdJzzz0ns9mssLAwSXef2nrz1M01a9bYa7r5z8313WsvAAAAgNvhit4DbOTIkYqOjtbjjz+ugQMHSroRsHr16qWQkBBNmTJFJ0+e1KhRo5QrVy4NGDAgzX28vb1ltVpVvHhxzZ49WwULFtSePXs0ZswY+fj4qEWLFhmubcaMGVq5cqWGDx+u6tWr68KFCzp58qTDNtOnT1d4eLgqVKigXLly6YsvvtDs2bM1ZswYVahQQYcOHdLo0aOVO3duhYSE6OrVq+rdu7fq1q2radOmKTY29o5TUIOCgjRixAjNmTNHGzdulCTlzp07w8dyq5UrV6pdu3ZaunSpypUrl6krkS1atFCDBg3sr3/55RcNHTpU1apVk6Qs7wXwsLBYLOneJj3bIuPor/PQW+eht85Ff50nq3ubkXEIeg+wfPnyycPDQ56envbpijNnzlTx4sU1ZswYubm5yc/PT+fPn9f06dPVv3//NPeRJJPJZA9+klSyZEnt3btXGzduzHDQ+++//7Rs2TKNGTNGISEhkqRSpUqlulfupZdeUpMmTeyvIyMjFR4ebl9WsmRJHTt2TCtWrFBISIjWr18vq9WqiRMnKleuXHr88cd17tw5RUREpFlHzpw5lS9fPrm5uWXpdM6UKZgFCxbM9Lienp7y9PSUJMXExOitt97S4MGDVa9ePUlZ3wvgYXHkyBElJCSka9sDBw44uZrsjf46D711HnrrXPTXeVzRW4LeQ+b48eMKCgqSm5ubfVn16tUVHx+vc+fOqUSJErfd96OPPtLq1at15swZXb9+XUlJSSpfvnyGazhx4oQSExNVu3btO25XuXJl+9/j4+MVExOjkSNHavTo0fblycnJ9nvfjh8/LrPZrFy5ctnXBwUFZbi+B8mVK1fUu3dvNWzYUD169JCUfXsBSDemTt+NxWLRgQMHFBAQIJPJdB+qyl7or/PQW+eht85Ff50nq3ubMl56EPSyia+++kpTpkzRsGHDFBQUpDx58ui9997Tvn37MjzWzeHjTm6eRhkfHy9JGjdunKpWreqwnbv7/b1V1M3NTTabzWFZyj2OWclisWjQoEHKmzevxo0bZ1/+IPUCuN8y8kPOZDLxHw4nor/OQ2+dh946F/11Hlf0lqD3gPPw8JDVarW/9vPz06ZNm2Sz2exX9Xbt2qU8efKoePHiae4jSbt371ZQUJA6depkXxYTE5OpmsqUKSNPT0/9/PPPKlmyZLr2KVKkiIoWLapTp06pdevWaW7j5+enzz//XNevX7eHyb17995xXA8PjwzPefb29taFCxfsr6Ojox2mkqXck3evc6knTZqko0ePavXq1Q7h2Fm9AAAAAFJw+eAB5+vrq3379ik2NlZxcXHq2LGjzp07p3Hjxun48ePasmWLIiMj1bVrV/vVoFv3sVqtKl26tH7//Xdt27ZNJ0+e1KxZszI9VzhXrlzq2bOnpk2bpnXr1ikmJkZ79+7VypUr77jfwIEDtXDhQi1btkwnT57UkSNHtHr1ai1ZskSS9Oyzz8rNzU2jRo3SsWPH9MMPP+j999+/a3/i4+O1c+dOxcXFpeven9q1a+ujjz7SwYMHdeDAAb355psOD1wpXLiwPD09tW3bNv3999+6cuVKOrriaPXq1fr44481duxYubm56a+//tJff/2lq1evOq0XAAAAQAqC3gOuW7duMplMatmyperUqaOkpCQtXLhQ+/fvV5s2bRQREaHQ0FD17dv3tvucOXNGHTp0UJMmTTR48GC1b99ely5dUseOHTNdV79+/dS1a1fNmTNHLVq00ODBgxUXF3fHfdq1a6fx48drzZo1atWqlcLCwrR27Vo9+uijkqQ8efJo/vz5Onr0qJ577jnNnDlTr7/++h3HrFatmjp06KBBgwapTp06Wrx48V1rHzZsmB555BF16tRJr7/+urp162Z/cIok5ciRQ6NGjdKKFSvUoEGDO36kwu38+uuvslgs6tu3r+rXr2//kxLWnNELAAAAIIWb7dablQAYhsVi0d69ezXr0FrtP3fM1eUgGytftIxWdJmcrm1TztvAwEDuFXEC+us89NZ56K1z0V/nyereZmQ8rugBAAAAgMHwMBakcubMGbVs2fK267/66qs7foyDK/3222/q2bPnbdfv2bPnPlYDAAAAuAZBD6kULVpU69atu+P6B1XlypXvWDsAAACQHRD0kEqOHDlUunRpV5eRKZ6eng9t7QAAAEBW4R49AAAAADAYrugB2UAZ7xJKtCa7ugxkY2UL+7q6BAAAshWCHpANRDTtzeOS4XIWq1UmdyaSAABwP/ATF8gGLBaLq0swHIvFooMHD9LbDCDkAQBw//BTFwAyKSEhwdUlAAAApImgBwAAAAAGQ9ADAAAAAIMh6AEAAACAwRD0AAAAAMBgCHoAAAAAYDAEPQAAAAAwGIIeAAAAABgMQQ8AAAAADIagBwAAAAAGQ9ADAAAAAIMh6AEAAACAwRD0AAAAAMBgCHoAAAAAYDAEPQAAAAAwGIIeAAAAABgMQQ8AAAAADIagBwAAAAAGQ9ADAAAAAIMh6AEAAACAwRD0AAAAAMBgCHoAAAAAYDAEPQDIJC8vL1eXAAAAkKYcri4AgPOZTCZXl2A4JpNJFStWdHUZDxSL1SqTO78/BADgQUDQA7KBiE0LdPTvGFeXAQMrW9hXk1oOcHUZAADg/yPoAdlAdNwZHb4Q7eoyAAAAcJ8wxwYAAAAADIagBwAAAAAGQ9ADAAAAAIMh6AEAAACAwRD0YAhRUVEym826fPnyXbdds2aNatSocR+qAgAAAFyDp24i22nRooUaNmzo6jIAAAAApyHoIdvx9PSUp6enq8sAAAAAnIapm7ijH3/8US+++KJq1KihWrVqqXfv3oqJufHB27GxsTKbzfrmm28UFhamqlWrqnXr1tqzZ499/5Rpktu2bVPz5s0VFBSk7t2768KFC/ZtwsLCNGHCBIf37devn8LDw+2v161bp7Zt2yooKEj16tXTkCFDdPHixUwd061TNyMjI9WmTRutW7dOwcHBql69ugYPHqz//vvPvo3VatWiRYvUuHFjVa5cWY0aNdK8efPs648cOaIuXbqoSpUqqlWrlkaPHq2rV6/a14eHh6tfv36aP3++6tatqxo1amju3LlKTk7WlClT9MQTT+jJJ5/U6tWrHWo9e/asXn31VdWoUUNPPPGE+vbtq9jY2EwdNwAAALIPgh7uKCEhQV27dtXq1au1dOlSubm5qX///rJarfZtZs6cqe7du2vdunUqU6aMhgwZouTkZPv6a9eu6f3339fUqVP14Ycf6uzZs5oyZUqG6khOTtarr76qL774Qu+8845Onz7tEATvVUxMjLZu3ar58+drwYIF+vXXX7Vo0SL7+hkzZmjRokXq16+fNmzYoOnTp6tIkSKSpPj4eHXv3l0FChTQqlWrNGvWLP30008aN26cw3v8/PPPunDhgj788EOFh4crMjJSvXv3VoECBfTZZ5+pQ4cOevPNN3Xu3DlJUlJSkrp37648efLoo48+0ieffKLcuXOrR48eSkxMzLJjBwAAgPEwdRN31LRpU4fXEydOVJ06dXTs2DHlzp1bktStWzc1atRIkjRw4EC1bNlSf/75p/z8/CTdCCxjx45VqVKlJEmdOnXSu+++m6E6QkND7X8vWbKkRo4cqdDQUF29elV58uTJ7OHZ2Ww2TZo0SXnz5pUktW7dWjt37rRf2Vu2bJnGjBmjkJAQSVKpUqXsVwXXr1+vxMRETZkyxd6TMWPGqE+fPnr99dftgbBgwYIaNWqU3N3dVbZsWS1evFjXrl1Tnz59JEm9e/fWokWLtGvXLrVs2VIbNmyQ1WrVhAkT5ObmJkmaNGmSatasqV9++UX169e/5+MGsprFYsmyMbJiLKRGf52H3joPvXUu+us8Wd3bjIxD0MMdRUdHa86cOdq3b5/++ecf2Ww2STemFKYEObPZbN/ex8dHkhQXF2df7+XlZQ95klS0aNEMT7v8/fffNXfuXB0+fFj//vuvQx3lypXL/AH+f76+vvaQd2uNJ06cUGJiomrXrp3mvsePH5fZbLaHPEmqVq2arFarTp48aQ965cqVk7v7/11EL1KkiB5//HH7a5PJpIIFC9rf9/Dhw4qJiVG1atUc3u/69ev26bPAg+bIkSNKSEjIkrEOHDiQJeMgbfTXeeit89Bb56K/zuOK3hL0cEd9+vSRr6+vxo8fr6JFi8pqterZZ59VUlKSfRsPDw/731OuPN08tTNHDsfTzM3NzR7U0notyWHqZ8rUyPr162v69OkqVKiQzp49q+7duzvUcS9urVGSvaZcuXI55T3c3NzSXJbSu/j4eFWqVEnTp09PNZa3t3eW1ARktZt/8ZNZFotFBw4cUEBAgEwmUxZUhZvRX+eht85Db52L/jpPVvc2Zbz0IOjhtv755x+dPHlS48ePt09T/O2337L8fby9vfXXX3/ZX1ssFv3xxx+qVauWpBtX1C5duqTXX39djzzyiKQbV/julzJlysjT01M///yzSpYsmWq9n5+f1q5dq/j4ePtVvd27d8vd3V2PPfZYpt+3UqVK+vrrr1W4cGGHq43Agywr/4NgMpn4D4cT0V/nobfOQ2+di/46jyt6y8NYcFsFChRQwYIFtWLFCv3555/auXOnJk+enOXvU7t2bf3www/6/vvvdfz4cUVERDh88HmJEiXk4eGh5cuX69SpU9q6dWuG7/G7F7ly5VLPnj01bdo0rVu3TjExMdq7d69WrlwpSWrVqpVy5syp8PBwHT16VD///LPGjRunNm3a2KdtZkarVq1UqFAh9e3bV7/99ptOnTqlqKgojR8/3v7AFgAAACAtXNHDbbm7u2vmzJkaP368nn32WT322GMaNWqUwsLCsvR9nn/+eR0+fFjDhg2TyWTSyy+/bL+aJ9244jd58mS9/fbbWr58uSpVqqRhw4apb9++WVrHnfTr108mk0lz5szRhQsX5OPjow4dOki6cQ/ie++9pwkTJig0NFReXl5q0qTJPT8V1MvLSx9++KGmT5+uV155RVevXlWxYsVUp04drvABAADgjtxst94cBcAwLBaL9u7dq1mH1mr/uWOuLgcGVr5oGa3okjVX/FPO28DAQKYQOQH9dR566zz01rnor/NkdW8zMh5TNwEAAADAYJi6CcPp0aOHdu3alea63r172z+3DgAAADAqgh4MZ8KECbp27Vqa6woUKHCfqwEAAADuP4IeDKdYsWKuLgEAAABwKe7RAwAAAACD4YoekA2U8S6hRGuyq8uAgZUt7OvqEgAAwE0IekA2ENG0N49LhtNZrFaZ3JkoAgDAg4CfyEA2YLFYXF2C4VgsFh08eJDe3oSQBwDAg4OfygCQSQkJCa4uAQAAIE0EPQAAAAAwGIIeAAAAABgMQQ8AAAAADIagBwAAAAAGQ9ADAAAAAIMh6AEAAACAwRD0AAAAAMBgCHoAAAAAYDAEPQAAAAAwGIIeAAAAABgMQQ8AAAAADIagBwAAAAAGQ9ADAAAAAIMh6AEAAACAwRD0AAAAAMBgCHoAAAAAYDAEPQAAAAAwGIIeAAAAABgMQQ8AAAAADIagBwAAAAAGQ9ADAAAAAIMh6AEAAACAwRD0ACCTvLy8XF0CAABAmnK4ugAAzmcymVxdguGYTCZVrFjR1WVkmsVqlcmd3/UBAGBUBD0gG4jYtEBH/45xdRl4QJQt7KtJLQe4ugwAAOBEBD0gG4iOO6PDF6JdXQYAAADuE+btAAAAAIDBEPQAAAAAwGAIegAAAABgMAQ9AAAAADAYgh6cJjw8XP369XN1GQ5iY2NlNpt16NAhSVJUVJTMZrMuX76c6TGDg4O1dOlS+2uz2awtW7bcU50PYu8AAADw8CDoZTNhYWGaMGGC0/d5WAQFBWn79u3Kly+fJGnNmjWqUaPGPY25fft2Pfnkk/c0xsiRIzV58mT7ayN/DQAAAJD1+HgFPJSSkpLk4eFxz+PkzJlTPj4+WVDR/8mK8VKCJwAAAJAZXNHLRsLDw/XLL79o2bJlMpvNMpvNio2N1S+//KLQ0FBVrlxZ9evX1/Tp05WcnHzHfSwWi0aMGKHg4GBVqVJFTZs21QcffJDp2qxWqxYtWqTGjRurcuXKatSokebNmyfp/6ZbbtiwQZ07d1ZAQIC+/PJLSdLKlSvVvHlzBQQEqFmzZvroo48cxt2/f7+ee+45BQQEqG3btvYpmylunroZFRWl4cOH68qVK/ZjjYyMzPCx3Dx18+baO3bsqCpVquj555/XyZMntX//frVt21ZBQUHq0aOH4uLi7GPcPHXzdl8DAAAA4Ha4opeNjBw5UtHR0Xr88cc1cOBASTcCVq9evRQSEqIpU6bo5MmTGjVqlHLlyqUBAwakuY+3t7esVquKFy+u2bNnq2DBgtqzZ4/GjBkjHx8ftWjRIsO1zZgxQytXrtTw4cNVvXp1XbhwQSdPnnTYZvr06QoPD1eFChWUK1cuffHFF5o9e7bGjBmjChUq6NChQxo9erRy586tkJAQXb16Vb1791bdunU1bdo0xcbG3nH6Y1BQkEaMGKE5c+Zo48aNkqTcuXNn+FjSEhkZqREjRqhEiRIaMWKEhgwZojx58mjkyJHy8vLSoEGDNHv2bI0dOzbVvrf7GgD3ymKxuLqENKXU9aDW97Cjv85Db52H3joX/XWerO5tRsYh6GUj+fLlk4eHhzw9Pe3TC2fOnKnixYtrzJgxcnNzk5+fn86fP6/p06erf//+ae4jSSaTyR46JKlkyZLau3evNm7cmOGg999//2nZsmUaM2aMQkJCJEmlSpVKda/cSy+9pCZNmthfR0ZGKjw83L6sZMmSOnbsmFasWKGQkBCtX79eVqtVEydOVK5cufT444/r3LlzioiISLOOnDlzKl++fHJzc8vy6ZzdunVTgwYNJEldunTRa6+9pqVLl6p69eqSpNDQUK1ZsybNfW/3NQDu1ZEjR5SQkODqMm7rwIEDri7B0Oiv89Bb56G3zkV/nccVvSXoZXPHjx9XUFCQ3Nzc7MuqV6+u+Ph4nTt3TiVKlLjtvh999JFWr16tM2fO6Pr160pKSlL58uUzXMOJEyeUmJio2rVr33G7ypUr2/8eHx+vmJgYjRw5UqNHj7YvT05Ott/fdvz4cZnNZuXKlcu+PigoKMP1ZQWz2Wz/e+HChdNcdvPUTeB+uPkcfJBYLBYdOHBAAQEBMplMri7HcOiv89Bb56G3zkV/nSere5syXnoQ9JApX331laZMmaJhw4YpKChIefLk0Xvvvad9+/ZleKybg9id3DyNMj4+XpI0btw4Va1a1WE7d/cH79bTmx8ckxKqc+TI4bDMarXe97qQvT3oP8xNJtMDX+PDjP46D711HnrrXPTXeVzRW4JeNuPh4eEQKPz8/LRp0ybZbDZ7ANm1a5fy5Mmj4sWLp7mPJO3evVtBQUHq1KmTfVlMTEymaipTpow8PT31888/q2TJkunap0iRIipatKhOnTql1q1bp7mNn5+fPv/8c12/ft0eJvfu3XvHcT08PB7I+elpfQ0AAACA23nwLn3AqXx9fbVv3z7FxsYqLi5OHTt21Llz5zRu3DgdP35cW7ZsUWRkpLp27Wq/MnbrPlarVaVLl9bvv/+ubdu26eTJk5o1a1am5x7nypVLPXv21LRp07Ru3TrFxMRo7969Wrly5R33GzhwoBYuXKhly5bp5MmTOnLkiFavXq0lS5ZIkp599lm5ublp1KhROnbsmH744Qe9//77d+1PfHy8du7cqbi4uAfm/qW0vgYAAADA7RD0splu3brJZDKpZcuWqlOnjpKSkrRw4ULt379fbdq0UUREhEJDQ9W3b9/b7nPmzBl16NBBTZo00eDBg9W+fXtdunRJHTt2zHRd/fr1U9euXTVnzhy1aNFCgwcPvus9a+3atdP48eO1Zs0atWrVSmFhYVq7dq0effRRSVKePHk0f/58HT16VM8995xmzpyp119//Y5jVqtWTR06dNCgQYNUp04dLV68ONPHlJXS+hoAAAAAt+Nms9lsri4CgHNYLBbt3btXsw6t1f5zx1xdDh4Q5YuW0Youk11dxm2lnLeBgYHcK+IE9Nd56K3z0Fvnor/Ok9W9zch4XNEDAAAAAIPhYSxwujNnzqhly5a3Xf/VV1/d8WMcXOm3335Tz549b7t+z54997EaAAAAIH0IenC6okWLat26dXdc/6CqXLnyHWsHAAAAHkQEPThdjhw5VLp0aVeXkSmenp4Pbe0AAADIvrhHDwAAAAAMhit6QDZQxruEEq3Jri4DD4iyhX1dXQIAAHAygh6QDUQ07c3jkuHAYrXK5M6kDgAAjIqf8kA2YLFYXF2C4VgsFh08ePCh7S0hDwAAY+MnPQBkUkJCgqtLAAAASBNBDwAAAAAMhqAHAAAAAAZD0AMAAAAAgyHoAQAAAIDBEPQAAAAAwGAIegAAAABgMAQ9AAAAADAYgh4AAAAAGAxBDwAAAAAMhqAHAAAAAAZD0AMAAAAAgyHoAQAAAIDBEPQAAAAAwGAIegAAAABgMAQ9AAAAADAYgh4AAAAAGAxBDwAAAAAMhqAHAAAAAAZD0AMAAAAAgyHoAQAAAIDBEPQAAAAAwGAIegCQSV5eXq4uAQAAIE05XF0AAOczmUyuLsFwTCaTKlas6Ooy0s1itcrkzu/2AADILgh6QDYQsWmBjv4d4+oy4CJlC/tqUssBri4DAADcRwQ9IBuIjjujwxeiXV0GAAAA7hPm8QAAAACAwRD0AAAAAMBgCHoAAAAAYDAEPQAAAAAwGIIenCIsLEwTJkxwdRkAAABAtkTQAx4CwcHBWrp0qavLAAAAwEOCoAcAAAAABkPQg9P9+++/Gjp0qGrWrKmqVauqR48eio6Otq//559/9Nprr6lBgwaqWrWqWrVqpfXr1zuMERYWpvHjx2vq1Kl64oknVK9ePUVGRqa7hiVLlqhVq1YKDAxUw4YNFRERoatXr9rXr1mzRjVq1NB3332npk2bqmrVqho4cKASEhK0du1aBQcHq2bNmho/frwsFku6jy0yMlJt2rRxqGXp0qUKDg62vw4PD1e/fv303nvvqX79+qpVq5bGjh2rpKQk+7GfPn1akyZNktlsltlsTvdxAwAAIHsi6MHpwsPD9fvvv2vevHlasWKFbDabevXqZQ8yiYmJqlSpkhYuXKj169erffv2Gjp0qPbv3+8wztq1a5U7d2599tlneuONN/TOO+9ox44d6arBzc1NI0eO1Pr16zV58mT9/PPPmjZtmsM2165d0/LlyzVz5kwtXrxYUVFReuWVV/TDDz9o4cKFmjp1qj799FNt2rQp3ceWXlFRUYqJidEHH3ygyZMna+3atVq7dq2kG2GxePHiGjhwoLZv367t27dnaGwAAABkPzlcXQCMLTo6Wt9++60++eQTVatWTZI0ffp0NWrUSFu2bFHz5s1VrFgxde/e3b5PWFiYtm/frq+//lpVqlSxLzebzXrllVckSWXKlNGHH36onTt3ql69enet4+WXX7b//dFHH9WgQYP05ptvKiIiwr48KSlJERERKlWqlCSpadOm+uKLL7Rjxw7lyZNH5cqVU61atfTzzz+rRYsW6Tq29CpQoIDGjBkjk8kkPz8/NWzYUDt37lT79u1VsGBBmUwm5cmTRz4+PukeE7jVzVejH2QpdT4s9T5s6K/z0FvnobfORX+dJ6t7m5FxCHpwquPHjytHjhyqWrWqfVmhQoX02GOP6fjx45JunLDz58/Xxo0bdf78eSUlJSkxMVGenp4OY906ZdHHx0cXL15MVx0//fSTFixYoBMnTui///6TxWLR9evXlZCQIC8vL0mSl5eXPeRJUpEiReTr66s8efI4LIuLi0v3saVXuXLlZDKZHI7t6NGjGRoDuJsjR44oISHB1WWk24EDB1xdgqHRX+eht85Db52L/jqPK3pL0IPLvffee1q2bJlGjBghs9ksLy8vTZw4MdX0xxw5HE9XNzc32Wy2u44fGxur3r1768UXX9TgwYNVoEAB7dq1SyNHjlRSUpI96KU1flrLrFZruo8trRqTk5NTbZfZYwMy4mG5v9NisejAgQMKCAhw+AUIsgb9dR566zz01rnor/NkdW9TxksPgh6cys/PT8nJydq3b599euM///yjkydPqly5cpKk3bt36+mnn7Y/tMRqtSo6Olp+fn5ZUsP//vc/2Ww2hYeHy939xm2pX3/99T2Pm55j8/b21t9//y2bzSY3NzdJ0qFDhzL8Xh4eHhkKmEBaHrYf3iaT6aGr+WFCf52H3joPvXUu+us8rugtD2OBU5UpU0ZPP/20Ro8erd9++02HDx/WG2+8oWLFiunpp5+WJJUuXVo//fSTdu/erePHj2vMmDH6+++/s6yG0qVLKykpScuXL9epU6e0bt06ffrpp/c8bnqOrVatWoqLi9OiRYsUExOjjz76SNu2bcvwe/n6+urXX3/V+fPn7VNHAQAAgNsh6MHpJk2apEqVKqlPnz564YUXZLPZtHDhQnl4eEiS+vbtq4oVK6p79+4KCwtTkSJF9Mwzz2TZ+5cvX17Dhw/XokWL9Oyzz+rLL7/Ua6+9liVj3+3Y/Pz89Oabb+rjjz9WmzZttH//fnXr1i3D7zNw4ECdPn1azzzzjOrUqZMltQMAAMC43GzcCAQYlsVi0d69ezXr0FrtP3fM1eXARcoXLaMVXSa7uox0SzlvAwMDmULkBPTXeeit89Bb56K/zpPVvc3IeFzRAwAAAACD4WEseOh98cUXevPNN9NcV6JECX311Vf3uSIAAADAtQh6eOgFBwc7fJbdzW792AIAAAAgO+B/wXjo5c2bV3nz5nV1GQAAAMADg3v0AAAAAMBguKIHZANlvEso0Zrs6jLgImUL+7q6BAAAcJ8R9IBsIKJpbx6XnM1ZrFaZ3JnEAQBAdsFPfSAbsFgsri7BcCwWiw4ePPjQ9JaQBwBA9sJPfgDIpISEBFeXAAAAkCaCHgAAAAAYDEEPAAAAAAyGoAcAAAAABkPQAwAAAACDIegBAAAAgMEQ9AAAAADAYAh6AAAAAGAwBD0AAAAAMBiCHgAAAAAYDEEPAAAAAAyGoAcAAAAABkPQAwAAAACDIegBAAAAgMEQ9AAAAADAYAh6AAAAAGAwBD0AAAAAMBiCHgAAAAAYDEEPAAAAAAyGoAcAAAAABkPQAwAAAACDIegBAAAAgMEQ9AAgk7y8vFxdAgAAQJpyuLoAAM5nMplcXYLhmEwmVaxY0dVl3JHFapXJnd/nAQCQHRH0gGwgYtMCHf07xtVl4D4qW9hXk1oOcHUZAADARQh6QDYQHXdGhy9Eu7oMAAAA3CfM6QEAAAAAgyHoAQAAAIDBEPQAAAAAwGAIegAAAABgMAQ9AAAAADAYgh7uSXh4uPr16+fqMgAAAADchKBnMGFhYZowYcJ92w9Zh9AMAACArELQAwAAAACDIejdg7CwMI0fP15Tp07VE088oXr16ikyMlKSFBsbK7PZrEOHDtm3v3z5ssxms6KioiRJUVFRMpvN2rZtm5577jlVqVJFXbp00cWLF/XDDz+oefPmqlatmoYMGaKEhIS71hMeHq5ffvlFy5Ytk9lsltlsVmxsrCTpl19+UWhoqCpXrqz69etr+vTpSk5OvuN+FotFI0aMUHBwsKpUqaKmTZvqgw8+yHS/rFarFi1apMaNG6ty5cpq1KiR5s2bZ19/7tw5vfbaa3riiScUGBiotm3bat++fXcdNyYmRn379lXdunUVFBSk559/Xj/99JPDNomJiZo2bZoaNmyoypUrq3Hjxlq5cqV9/R9//KHevXurWrVqCgoKUseOHRUTE2Ove+7cuXryySdVuXJltWnTRj/++KN935Sv4+XLl+3LDh065ND/NWvWqEaNGtq2bZuaN2+uoKAgde/eXRcuXJAkRUZGau3atdq6dav9axAVFaXExES99dZbql+/vgICAvTUU09pwYIFmeg+AAAAspMcri7gYbd27Vp17dpVn332mfbu3avw8HBVq1ZNpUuXTvcYc+fO1ejRo+Xl5aVBgwZp0KBBypkzp2bMmKH4+Hj1799fy5cvV69eve44zsiRIxUdHa3HH39cAwcOlCR5e3vr/Pnz6tWrl0JCQjRlyhSdPHlSo0aNUq5cuTRgwIDb7me1WlW8eHHNnj1bBQsW1J49ezRmzBj5+PioRYsWGe7VjBkztHLlSg0fPlzVq1fXhQsXdPLkSUnS1atX1blzZxUrVkzvvvuufHx89L///U9Wq/Wu48bHx6thw4YaPHiwcubMqXXr1qlPnz7auHGjSpQoIUkaOnSo9u7dq1GjRql8+fKKjY3VP//8I0k6f/68OnfurCeeeEIffPCB8ubNq927d9uD8LJly7RkyRK99dZbqlChglavXq1+/fpp/fr1KlOmTLqP/9q1a3r//fc1depUubu764033tCUKVM0Y8YMdevWTcePH9d///2nSZMmSZIKFCig5cuX69tvv9WsWbP0yCOP6OzZszp37lxG2o5szmKxuLqEDEup+WGs/WFAf52H3joPvXUu+us8Wd3bjIxD0LtHZrNZr7zyiiSpTJky+vDDD7Vz584MBb1BgwapevXqkqTQ0FDNmDFDW7ZsUcmSJSVJTZs2VVRU1F2DXr58+eTh4SFPT0/5+PjYl3/88ccqXry4xowZIzc3N/n5+en8+fOaPn26+vfvf9v9TCaTPfhJUsmSJbV3715t3Lgxw0Hvv//+07JlyzRmzBiFhIRIkkqVKqUaNWpIktavX6+4uDitWrVKBQsWlKR097B8+fIqX768/fWgQYO0ZcsWffvtt+rcubNOnjypr7/+WkuWLFHdunXtx5Lio48+Ut68efX222/Lw8NDkvTYY4/Z17/33nvq2bOnWrZsKUl64403FBUVpQ8++EBvvvlmunuQlJSksWPHqlSpUpKkTp066d1335Uk5cmTR56enkpMTHT4Gpw9e1alS5dW9erV5ebmJl9f33S/HyBJR44cSdeMgAfRgQMHXF2CodFf56G3zkNvnYv+Oo8rekvQu0dms9nhtY+Pjy5evJjpMQoXLiwvLy+HIFKkSJF7OjmOHz+uoKAgubm52ZdVr15d8fHxOnfunP2qV1o++ugjrV69WmfOnNH169eVlJTkEKrS68SJE0pMTFTt2rXTXH/o0CFVrFjRHvIy4urVq5o7d66+//57/fXXX7JYLLp27ZrOnDljH9tkMqlmzZq3fe8aNWrYQ97N/vvvP124cEHVqlVzWF6tWjUdPnw4Q3V6eXnZQ54kFS1a9K7nSkhIiLp166ZmzZqpQYMGatSokerXr5+h90X2duv3qIeBxWLRgQMHFBAQIJPJ5OpyDIf+Og+9dR5661z013myurcp46UHQe8e5cjh2EI3NzfZbDa5u9+4/dFms9nXpUwFvNMYbm5uaY6ZnimMWe2rr77SlClTNGzYMAUFBSlPnjx677330nXf3K1y5cp1x/Wenp6ZLVNTpkzRTz/9pGHDhqlUqVLy9PTUwIEDlZSUlK6x7+W9JaX5tU5575vd7ly5k0qVKmnr1q368ccf9dNPP2nQoEGqW7eu5syZc081I/t4mH9gm0ymh7r+Bx39dR566zz01rnor/O4orc8jMVJvL29JUl//fWXfdnND2ZxFg8Pj1Sh0M/PT3v27HEIFbt27VKePHlUvHjx2+63e/duBQUFqVOnTqpYsaJKly5tf0BJRpUpU0aenp76+eef01yf8uCaS5cuZXjsPXv2KCQkRI0bN5bZbFaRIkV0+vRp+3p/f39ZrVb9+uuvt33v3377Lc1wljdvXhUtWlS7d+92WL57926VK1dOUtpf64xe7ZPS/hqk1NCiRQuNHz9eM2fO1KZNmzLVJwAAAGQfBD0n8fT0VGBgoBYuXKjjx4/rl19+0axZs5z+vr6+vtq3b59iY2MVFxcnq9Wqjh076ty5cxo3bpyOHz+uLVu2KDIyUl27drVfjUprv9KlS+v333/Xtm3bdPLkSc2aNSvTU0hz5cqlnj17atq0aVq3bp1iYmK0d+9e+5MvW7ZsqSJFiqh///7atWuXTp06pU2bNmnPnj13Hbt06dLavHmzDh06pMOHD2vIkCEOgenRRx9VSEiIRowYoS1btujUqVOKiorShg0bJN24V+6///7Ta6+9pgMHDig6Olrr1q3TiRMnJEndu3fXokWLtGHDBp04cULTp0/X4cOH1aVLF0k37jV85JFHFBkZqejoaH3//fd6//33M9wjX19fHTlyRCdOnFBcXJySkpK0ZMkSrV+/XsePH9fJkye1ceNG+fj4KH/+/BkeHwAAANkHQc+JJk6cKIvForZt22rixIkaNGiQ09+zW7duMplMatmyperUqaMzZ86oWLFiWrhwofbv3682bdooIiJCoaGh6tu37x3369Chg5o0aaLBgwerffv2unTpkjp27Jjp2vr166euXbtqzpw5atGihQYPHqy4uDhJUs6cOfX++++rcOHC6tWrl1q1aqWFCxem6xJ3eHi48ufPrw4dOqhPnz5q0KCBKlWq5LBNRESEmjZtqoiICDVv3lyjR4+2P6CiUKFC+uCDDxQfH6+wsDC1bdtWK1eutN+z16VLF3Xt2lWTJ09W69attW3bNr377rv2J256eHhoxowZOnHihFq3bq1FixZl6mvdvn17PfbYY3r++edVp04d7d69W3ny5NHixYsVGhqq0NBQnT59WgsXLrQHdAAAACAtbra73SQE4KFlsVi0d+9ezTq0VvvPHXN1ObiPyhctoxVdJru6jExJOW8DAwO5V8QJ6K/z0FvnobfORX+dJ6t7m5HxuCwAAAAAAAbDUzcfImfOnLF/lltavvrqqzt+VIIzObO2li1b2j8q4VZjx45V69atMzUuAAAAYFQEvYdI0aJFtW7dujuudxVn1rZw4cLbfjRF4cKFMz0uAAAAYFQEvYdIjhw5VLp0aVeXkSZn1ubr6+uUcQEAAACj4h49AAAAADAYrugB2UAZ7xJKtKY9/RXGVLYwV8IBAMjOCHpANhDRtDePS86GLFarTHzmIgAA2RL/AwCyAYvF4uoSDMdisejgwYMPdG8JeQAAZF/8LwAAMikhIcHVJQAAAKSJoAcAAAAABkPQAwAAAACDIegBAAAAgMEQ9AAAAADAYAh6AAAAAGAwBD0AAAAAMBiCHgAAAAAYDEEPAAAAAAyGoAcAAAAABkPQAwAAAACDIegBAAAAgMEQ9AAAAADAYAh6AAAAAGAwBD0AAAAAMBiCHgAAAAAYDEEPAAAAAAyGoAcAAAAABkPQAwAAAACDIegBAAAAgMEQ9AAAAADAYAh6AAAAAGAwBD0AyCQvLy9XlwAAAJCmHK4uAIDzmUwmV5dgOCaTSRUrVnR1GbJYrTK58zs7AADgiKAHZAMRmxbo6N8xri4DWaxsYV9NajnA1WUAAIAHEEEPyAai487o8IVoV5cBAACA+4T5PgAAAABgMAQ9AAAAADAYgh4AAAAAGAxBDwAAAAAMhqCHbCMyMlJt2rSxvw4PD1e/fv0yPV5UVJTMZrMuX74sSVqzZo1q1Khxz3UCAAAA94qnbiLbGjlypGw2m/11WFiYypcvr5EjR2ZqvBYtWqhhw4ZZVR4AAACQaQQ9PFQSExOVM2fOLBkrX758WTJOCk9PT3l6embpmAAAAEBmMHXT4BITEzV+/HjVqVNHAQEBevHFF7V//35J/zf18Pvvv1erVq0UEBCg9u3b6+jRow5j/Pbbb+rYsaOqVKmihg0bavz48YqPj7evDw4O1vz58zV8+HAFBQWpUaNGWrFiRbprPHfunF577TU98cQTCgwMVNu2bbVv3z5J/zfdcuXKlQoODlaVKlUkSZcvX9bIkSNVu3ZtVatWTV26dNHhw4cdxl24cKHq1q2roKAgjRgxQtevX3dYf/PUzfDwcP3yyy9atmyZzGazzGazYmNj030MUuqpmym1r1u3TsHBwapevboGDx6s//77z76N1WrVggUL7MfWunVrbdy40b7+33//1ZAhQ1S7dm1VqVJFTZo00erVqzNUFwAAALIfgp7BTZ06VZs2bdLkyZO1du1alS5dWj169NClS5cctgkPD9eqVavk7e2tPn36KCkpSZIUExOjnj17qkmTJvriiy80c+ZM7dq1S+PGjXN4nyVLlqhy5cpat26dOnbsqIiICJ04ceKu9V29elWdO3fW+fPn9e677+rzzz9Xjx49ZLVa7dvExMRo06ZNmjt3rtatWydJevXVV3Xx4kUtWrRIa9asUaVKlfTSSy/Zj2vDhg2KjIzU4MGDtXr1avn4+Ojjjz++bR0jR45UUFCQ2rdvr+3bt2v79u165JFH0tnl24uJidHWrVs1f/58LViwQL/++qsWLVpkX79gwQKtW7dOY8eO1VdffaWXX35Zb7zxhn755RdJ0uzZs3X8+HEtWrRIGzZsUEREhAoVKnTPdQEAAMDYmLppYPHx8fr00081adIk+71j48aN044dO7Rq1SoFBARIkl555RXVq1dPkjR58mQ1bNhQmzdvVosWLbRgwQK1atVKL7/8siSpTJkyGjlypMLCwhQREaFcuXJJkp588kl16tRJktSzZ08tXbpUUVFRKlu27B1rXL9+veLi4rRq1SoVLFhQklS6dGmHbZKSkjR16lR5e3tLunGFcf/+/dq5c6d9GuewYcO0ZcsWbdq0SS+88IKWLVum0NBQtWvXTpI0ePBg7dy5M9VVvRT58uWTh4eHPD095ePjk+4e343NZtOkSZOUN29eSVLr1q21c+dODR48WImJiVqwYIGWLFmioKAgSVLJkiW1a9curVixQk888YTOnDmjChUq2L9Wjz76aJbVBuOwWCyuLiFLpRyP0Y7rQUF/nYfeOg+9dS766zxZ3duMjEPQM7CYmBglJSWpWrVq9mUeHh6qUqWKjh8/bg8PgYGB9vUFCxbUY489Zr8ad/jwYR05ckRffvmlfRubzSar1arY2Fj5+flJksxms329m5ubihQpoosXL961xkOHDqlixYr2kJeWEiVK2EOeJB05ckTx8fGqVauWw3bXrl1TTEyMJOn48ePq0KGDw/rAwEBFRUXdtaas5Ovraw95klS0aFF7X/78808lJCSoW7duDvskJSWpQoUKkqQXX3xRAwcO1MGDB1WvXj0988wzDl9PQLrxbyIhIcHVZWS5AwcOuLoEQ6O/zkNvnYfeOhf9dR5X9JaghzuKj49Xhw4dFBYWlmrdzVMbc+RwPJXc3Nwcnmh5O+l5eImXl5fD66tXr8rHx0fLly9PtW1WP2DlXt3aF0n2vqTc57hgwQIVK1bMYZuUK5UNGzbUd999px9++EE7duzQyy+/rE6dOmnYsGFOrhwPk5t/0WIEFotFBw4cUEBAgEwmk6vLMRz66zz01nnorXPRX+fJ6t6mjJceBD0DK1WqlDw8PLR79275+vpKunG16MCBA3rppZfs2+3du1clSpSQdOPhH9HR0fYplxUrVtSxY8dSTafMKmazWStXrtSlS5fueFXvZpUqVdLff/8tk8l026mMfn5+2rdvn5577jn7spQHvNyOh4eHw72Bzubn56ecOXPqzJkzeuKJJ267nbe3t0JCQhQSEqJPP/1UU6dOJejBgVF/KJtMJsMe24OA/joPvXUeeutc9Nd5XNFbHsZiYLlz59aLL76oqVOn6scff9SxY8c0evRoXbt2TaGhofbt3n33Xe3cuVNHjx5VeHi4ChUqpGeeeUbSjfvt9uzZo7feekuHDh1SdHS0tmzZorfeeitLamzZsqWKFCmi/v37a9euXTp16pQ2bdqkPXv23HafunXrKjAwUP3799f27dsVGxur3bt3a+bMmfbfcHTp0kWrV6/W6tWrdfLkSc2ZM0d//PHHHWvx9fXVvn37FBsbq7i4OKeHvrx586pbt26aNGmS1q5dq5iYGP3vf//T8uXLtXbtWkk3HsayZcsW/fnnn/rjjz/0/fff26fLAgAAALfDFT2De/3112Wz2TR06FBdvXpVlStX1uLFi1WgQAH7NkOGDNGECRMUHR2tChUqaN68efapg+XLl9fy5cs1a9YsdezYUdKNB4a0aNEiS+rLmTOn3n//fU2ZMkW9evWSxWKRn5+f3nzzzdvu4+bmpoULF2rWrFkaPny4/vnnHxUpUkQ1atRQkSJFJN348PKYmBhNmzZN169fV9OmTfXiiy9q+/bttx23W7duCg8PV8uWLXXt2jVt3brV6Q8/GTRokLy9vbVgwQLFxsYqX758qlixovr06SPpxlXGt99+W6dPn5anp6eqV6+ut99+26k1AQAA4OHnZkvPjVQwpKioKHXp0kW//vqr8ufP7+py4AQWi0V79+7VrENrtf/cMVeXgyxWvmgZregy2dVlZLmU8zYwMJApRE5Af52H3joPvXUu+us8Wd3bjIzH1E0AAAAAMBimbsKpUj4oPC3Vq1fX4sWL73NF6TdmzBiHj5W4WatWrbLsPkUAAAAgqxH0srFatWrpyJEjTn2PDh06qHnz5mmuS89HK7jSq6++qu7du6e57ubPxgMAAAAeNAQ9OFXBggXT/bEJD5rChQurcOHCri4DAAAAyDDu0QMAAAAAg+GKHpANlPEuoURrsqvLQBYrW9jX1SUAAIAHFEEPyAYimvbmcckGZbFaZXJncgYAAHDE/w6AbMBisbi6BMOxWCw6ePCgy3tLyAMAAGnhfwgAkEkJCQmuLgEAACBNBD0AAAAAMBiCHgAAAAAYDEEPAAAAAAyGoAcAAAAABkPQAwAAAACDIegBAAAAgMEQ9AAAAADAYAh6AAAAAGAwBD0AAAAAMJgcri4AgPPYbDZJksVikcVicXE1xpLST/qa9eitc9Ff56G3zkNvnYv+Ok9W9zZlnJT/492Jmy09WwF4KCUmJurAgQOuLgMAAABZKCAgQDlz5rzjNgQ9wMCsVquSk5Pl7u4uNzc3V5cDAACAe2Cz2WS1WpUjRw65u9/5LjyCHgAAAAAYDA9jAQAAAACDIegBAAAAgMEQ9AAAAADAYAh6AAAAAGAwBD0AAAAAMBiCHgAAAAAYDEEPAAAAAAyGoAc8wD766CMFBwcrICBA7dq10/79+++4/ddff61mzZopICBArVq10g8//OCw3mazafbs2apfv76qVKmil19+WdHR0Q7bXLp0SUOGDFG1atVUo0YNjRgxQlevXs3qQ3O5rOxtUlKSpk2bplatWikwMFD169fX0KFDdf78eYcxgoODZTabHf4sXLjQKcfnall97oaHh6fqXffu3R224dxN2916e2tfU/4sXrzYvk12OXcz0ts//vhDAwYMsPdm6dKlmRrz+vXrGjt2rGrVqqWgoCANGDBAf//9d1Ye1gMhq3u7YMECPf/88woKClKdOnXUr18/nThxwmGbsLCwVOftmDFjsvrQHghZ3d/IyMhUvWvWrJnDNpy7qaWnt2l9PzWbzRo7dqx9myw7d20AHkhfffWVrVKlSrZVq1bZ/vjjD9uoUaNsNWrUsP39999pbr9r1y5bhQoVbIsWLbIdO3bMNnPmTFulSpVsR44csW+zYMECW/Xq1W2bN2+2HTp0yNanTx9bcHCw7dq1a/ZtunfvbmvdurVt7969tl9//dXWuHFj22uvveb0472fsrq3ly9ftr388su2r776ynb8+HHbnj17bKGhobaQkBCHcZ566inb3LlzbRcuXLD/uXr1qtOP935zxrk7bNgwW/fu3R16d+nSJYdxOHdTS09vb+7phQsXbKtWrbKZzWZbTEyMfZvscO5mtLf79u2zTZ482bZ+/XpbvXr1bEuWLMnUmGPGjLE1bNjQ9tNPP9kOHDhga9++ve2FF15w1mG6hDN6261bN9vq1attR48etR06dMjWs2dPW6NGjRzOy86dO9tGjRrlcN5euXLFWYfpMs7o75w5c2wtW7Z06N3FixcdtuHcTS09vb148aJDX3fs2GHz9/e3/fzzz/ZtsurcJegBD6jQ0FDb2LFj7a8tFoutfv36tgULFqS5/auvvmrr1auXw7J27drZRo8ebbPZbDar1WqrV6+ebfHixfb1ly9ftlWuXNm2fv16m81msx07dszm7+9v279/v32bH374wWY2m23nzp3LsmNztazubVr27dtn8/f3t50+fdq+7Kmnnkrzm77ROKO/w4YNs/Xt2/e278m5m3Xnbt++fW1dunRxWJYdzt2M9vZmt+vP3ca8fPmyrVKlSravv/7avk3Kubxnz57MH8wDxhm9vdXFixf/X3v3HtvT/cdx/NmLzuhFtWVrai3LtJtSLYa6dG41WtbWkExrSMyCYEgxt6FW2zpxacnGSglCaUW3bottIVvULYrVGnda69CWotqs198f4vx8V9uob2m/ez0SyennfM7nfM4773xz3t/zOV/Vbdu2rT58+LDRFhkZWR0TE1OrOTckdRHfVatWVQ8dOvRvj1Pumi93Y2Jiqvv3719dVVVltJkrd7V0U6QeKisr49SpUwQGBhpt1tbWBAYGkpmZ+dBjjh8/Tvfu3U3aevbsyfHjxwG4cuUK+fn5JmM6ODjg5+dnjJmZmYmjoyPt27c3+gQGBmJtbf2vy8MairqI7cMUFxdjZWWFo6OjSfu6devo2rUrYWFhrF+/noqKitpfTD1Ul/E9fPgw3bt3Z+DAgSxcuJCbN28a+5S75sndgoIC9u/fz9tvv11jnyXnbm1ia44xs7KyKC8vN+nz8ssv4+7u/o+fLw1JXcT2Ye7cuQOAk5OTSXtaWhpdu3YlNDSUzz//nNLSUrOdsz6oy/hevnyZnj170q9fP2bMmEFeXp6xT7lrntwtKytjz549DBs2DCsrK5N95shdW7PMUkTM6ubNm1RWVuLi4mLS7uLiUuMdhPsKCgpwdXWt0f/+evn8/Hyj7e/6FBQU0Lx5c5P9tra2ODk5Gcc3dHUR27/6888/iYuLIyQkBHt7e6M9KiqK1157DScnJzIzM1m+fDn5+fnMmTPnCa+q/qir+Pbq1YsBAwbg4eFBbm4uy5cvZ/z48Wzfvh0bGxvlrplyNzU1laZNmxIcHGzSbum5W5vYmmPMgoICGjVqVOMLIRcXl/903j6uqqoqPv74YwICAmjbtq3RHhoairu7Oy1atOD06dPExcVx8eJF4uPjzXLe+qCu4tuhQwdiY2Np3bo1+fn5JCQkMGrUKNLS0rC3t1fumil3f/jhB+7cuUN4eLhJu7lyV4WeiIgZlZeXM3XqVKqrq01erAYYO3asse3j40OjRo1YuHAhM2bMwM7O7mlPtUEJCQkxtu+/mN6/f3/jKZ+Yx65duxgyZAjPPfecSbtyV+qzRYsWcfbsWbZu3WrSPnLkSGPb29sbNzc3xowZQ05ODi+99NLTnmaDEhQUZGz7+Pjg5+dHnz59+Pbbbxk+fPgznJll2bVrF71796Zly5Ym7ebKXS3dFKmHnJ2dsbGxobCw0KS9sLCwxrfz97m6utb4lv7B/m5ubkbb3/VxdXXlxo0bJvsrKiq4deuWcXxDVxexva+8vJxp06aRl5dHYmKiydO8h/Hz86OiooIrV67U4krqp7qM74NatWqFs7Mzly9fNsZQ7tb0OLE9evQoFy9efKSbOEvL3drE1hxjurq6Ul5ezu3bt2v0+S/n7eNYvHgx+/btIykpiRdeeOEf+/r5+QEYnxuWoK7je5+joyNeXl7k5OQAyl1zxPb333/nwIEDD10q/1e1zV0VeiL1kJ2dHe3atSMjI8Noq6qqIiMjA39//4ce07FjRw4ePGjSduDAATp27AiAh4cHbm5uJmMWFxdz4sQJY0x/f39u375NVlaW0efgwYNUVVXRoUMHc13eM1UXsYX/F3mXL19m48aNODs7/+tcsrOzsba2rrEspCGrq/j+1dWrVykqKjJuKJS7Tx7bnTt30q5dO3x8fP51LpaWu7WJrTnG9PX1pVGjRiZ9Lly4QF5e3j/mf0NSF7GFe/9d0OLFi9m7dy9JSUm0atXqX4/Jzs4GsJhCBOouvn919+5dcnNzjdgpd588tikpKbi4uPDGG2/8a9/a5q6WborUU2PHjmXWrFn4+vrSoUMHkpKSKC0tJSIiAoDo6GhatmzJjBkzABg9ejRRUVEkJiYSFBREeno6WVlZLF68GAArKytGjx7N2rVr8fT0xMPDg5UrV9KiRQv69+8P3HuRulevXsyfP59FixZRXl7OkiVLCAkJqbGsoCEzd2zLy8uZMmUKv/32G1988QWVlZXGOwpOTk7Y2dmRmZnJiRMn6NatG02bNiUzM5PY2FiGDh1a48cDGjpzx/fu3bvEx8czcOBAXF1dyc3N5bPPPsPT05NevXoByt3axva+4uJivvvuO2bNmlXjnP+V3H3c2JaVlXH+/Hlj+9q1a2RnZ9OkSRM8PT0faUwHBweGDRvGsmXLcHJywt7enpiYGPz9/S3mZhnqJraLFi3i66+/Zs2aNTRt2tT4zHVwcKBx48bk5OSQlpZGUFAQzZo14/Tp08TGxtKlS5dH+jKjIamL+H7yySf06dMHd3d3rl+/zurVq7G2tiY0NBRQ7j5JbOFewZiSkkJYWBi2tqblmDlzV4WeSD01ePBgbty4wapVq8jPz+fVV19l/fr1xnKBP/74A2vr/z+UDwgIIC4ujhUrVrB8+XK8vLxISEgweTF9/PjxlJaWsmDBAm7fvk2nTp1Yv369yfs4cXFxLFmyhHfffRdra2uCg4OZN2/e07vwp8Dcsb127Ro//fQTAG+99ZbJuTZt2kTXrl2xs7MjPT2d+Ph4ysrK8PDwYMyYMSbvPlkKc8fXxsaGM2fOsHv3bu7cuUOLFi3o0aMHU6dONXk/TLlbu88FgG+++Ybq6mrjJu5B/5XcfdzYXr9+nbCwMOPvxMREEhMTef3119m8efMjjQnw4YcfYm1tzZQpUygrK6Nnz54sXLjw6Vz0U1IXsd22bRtw74eCHhQbG0tERITxtGnTpk2UlJTw4osvEhwczMSJE+v4ap++uojv1atXmT59OkVFRTRv3pxOnTqxY8cOkx+9Uu7WLrZwb2VFXl4ew4YNq3FOc+auVXV1dfVjHyUiIiIiIiL1lt7RExERERERsTAq9ERERERERCyMCj0RERERERELo0JPRERERETEwqjQExERERERsTAq9ERERERERCyMCj0RERERERELo0JPRERERETEwqjQExERERERsTAq9EREROSRzZ49m4kTJz7raTzUlStX8Pb2Jjs7+1lPRUTkmVOhJyIiIg1eWVnZs56CiEi9okJPREREaiUqKoolS5awdOlSunTpQmBgIDt27KCkpIQ5c+bg7+/PgAED2L9/v3HMoUOH8Pb2Zt++fQwZMoT27dszYsQIzpw5YzL2999/T0hICL6+vvTt25fExEST/X379iUhIYHo6GgCAgJYsGAB/fr1AyAsLAxvb2+ioqIAOHnyJGPHjqVr16506tSJyMhITp06ZTKet7c3ycnJTJo0CT8/P4KDg/nxxx9N+pw9e5YJEyYQEBCAv78/77zzDjk5Ocb+5ORkBg0aRPv27XnzzTfZsmXLkwdZRKSWVOiJiIhIraWmpuLs7ExycjKRkZF89NFHTJ06FX9/f1JTU+nRowfR0dGUlpaaHPfpp58ye/Zsdu7cSfPmzXn//fcpLy8HICsri2nTpjF48GDS0tKYPHkyK1euJCUlxWSMxMREfHx82L17NxMnTiQ5ORmAjRs38ssvv7B69WoA7t69S1hYGFu3bmXHjh14enry3nvvUVxcbDJefHw8gwYNYs+ePfTu3ZuZM2dSVFQEwLVr14iMjMTOzo6kpCRSUlIYNmwYFRUVAOzZs4eVK1fywQcfkJ6ezvTp01m1ahWpqalmj7mIyKOwfdYTEBERkYbLx8fHeGdvwoQJrFu3DmdnZ0aMGAHApEmT2LZtG6dPn6Zjx47GcZMnT6ZHjx4ALFu2jKCgIPbu3cvgwYPZsGED3bt3Z9KkSQC0bt2ac+fO8dVXXxEREWGM0a1bN8aNG2f8bW197/vrZs2a4ebmZrR3797dZM5Lliyhc+fOHDlyhD59+hjt4eHhhIaGAjB9+nQ2b97MyZMn6d27N1u2bMHe3p7ly5fTqFEjY173rV69mtmzZxMcHAxAq1atOHfuHNu3byc8PLw2oRUReSIq9ERERKTWvL29jW0bGxuaNWtG27ZtjTZXV1cACgsLTY57sOhr1qwZrVu35sKFCwBcuHDBWIZ5X0BAAJs2baKyshIbGxsAfH19H2mOBQUFrFixgsOHD1NYWEhVVRWlpaXk5eX97bU0adIEe3t7bty4AUB2djadO3c2irwHlZSUkJOTw9y5c5k/f77RXlFRgYODwyPNUUTE3FToiYiISK3Z2preSlhZWZm0WVlZAVBdXW32cz///POP1G/WrFkUFRUxd+5c3N3dsbOzY+TIkcZS0fv+WsRZWVlRVVUFQOPGjf92/JKSEuDek0I/Pz+TffefMoqIPG369BEREZGn7vjx48b2rVu3uHTpEm3atAGgTZs2HDt2zKT/sWPH8PLyMp7mPcz9Qq2ysrLGsVFRUQQFBfHKK69gZ2fHzZs3H2u+3t7eHD16tEZxCPeeWrZo0YLc3Fw8PT1N/rVq1eqxziMiYi4q9EREROSpW7NmDRkZGZw5c4bZs2fj7OxM//79ARg3bhwZGRkkJCRw8eJFUlNT2bJli8n7eA/j4uJC48aN+fnnnykoKODOnTsAeHl5sWfPHs6fP8+JEyeYOXPmPz6he5hRo0ZRXFzM9OnT+fXXX7l06RK7d+82lptOmTKFL7/8kk2bNnHx4kVOnz7Nrl272LBhQy2iIyLy5FToiYiIyFM3Y8YMli5dSkREBAUFBaxduxY7OzsA2rVrx4oVK0hPT2fIkCGsWrWKKVOmmPwQy8PY2toyb948tm/fTq9evYwfiVm6dCm3bt0iPDyc6OhooqKicHFxeaz5Ojs7k5SURElJCVFRUURERJCcnGw8RRw+fDgxMTGkpKQwZMgQoqKiSE1NxcPDoxbRERF5clbVdbFoXkREROQhDh06xOjRozly5AiOjo7PejoiIhZLT/REREREREQsjAo9ERERERERC6OlmyIiIiIiIhZGT/REREREREQsjAo9ERERERERC6NCT0RERERExMKo0BMREREREbEwKvREREREREQsjAo9ERERERERC6NCT0RERERExMKo0BMREREREbEwKvREREREREQszP8AsEGMe16ep/YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>interest_rate</th>\n",
              "      <td>0.172174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>debt_to_income</th>\n",
              "      <td>0.062895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>installment</th>\n",
              "      <td>0.054788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>months_since_last_credit_inquiry</th>\n",
              "      <td>0.054584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_credit_utilized</th>\n",
              "      <td>0.046594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annual_income</th>\n",
              "      <td>0.044482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_credit_limit</th>\n",
              "      <td>0.038350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_amount</th>\n",
              "      <td>0.037230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_total_cc_accounts</th>\n",
              "      <td>0.036402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_credit_lines</th>\n",
              "      <td>0.034333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ],
            "text/plain": [
              "interest_rate                       0.172174\n",
              "debt_to_income                      0.062895\n",
              "installment                         0.054788\n",
              "months_since_last_credit_inquiry    0.054584\n",
              "total_credit_utilized               0.046594\n",
              "annual_income                       0.044482\n",
              "total_credit_limit                  0.038350\n",
              "loan_amount                         0.037230\n",
              "num_total_cc_accounts               0.036402\n",
              "open_credit_lines                   0.034333\n",
              "dtype: float64"
            ]
          },
          "execution_count": 123,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Random Forest built-in feature importance, top 10 features\n",
        "feature_importance = pd.Series(\n",
        "    rf_tuned.feature_importances_, index=X_train.columns\n",
        ").sort_values(ascending=False)\n",
        "\n",
        "top_10_importance = feature_importance.head(10)\n",
        "\n",
        "plt.figure(figsize=(9, 6))\n",
        "top_10_importance.sort_values().plot(kind=\"barh\", color=\"seagreen\")\n",
        "plt.title(\"Top 10 Feature Importances: Random Forest\")\n",
        "plt.xlabel(\"Importance\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "top_10_importance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0e465817",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0e465817",
        "outputId": "e334d257-b563-4a13-db97-59deac1cf59e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SHAP values computed for 500 test samples.\n"
          ]
        }
      ],
      "source": [
        "# Compute SHAP values for the Random Forest classifier using a representative test sample.\n",
        "\n",
        "shap_sample = X_test.sample(\n",
        "    n=min(500, len(X_test)),\n",
        "    random_state=RANDOM_STATE\n",
        ")\n",
        "\n",
        "explainer = shap.TreeExplainer(rf_tuned)\n",
        "shap_values = explainer.shap_values(shap_sample)\n",
        "\n",
        "if isinstance(shap_values, list):\n",
        "    shap_values_positive_class = shap_values[1]\n",
        "else:\n",
        "    shap_values_positive_class = shap_values\n",
        "\n",
        "print(\"SHAP values computed for\", shap_sample.shape[0], \"test samples.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1ee86543",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        },
        "id": "1ee86543",
        "outputId": "ece93fcc-6d3e-47f5-82c9-2d7508c3d8e1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1150x660 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# SHAP summary plot: overall feature impact and direction\n",
        "shap.summary_plot(shap_values_positive_class, shap_sample, show=False)\n",
        "plt.title(\"SHAP Summary Plot: Feature Impact on Default Risk Prediction\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0698c829",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        },
        "id": "0698c829",
        "outputId": "ad117056-b1eb-4d88-a0d9-7bf16c00ca7b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1150x660 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# SHAP bar plot: mean absolute impact, top 10 features\n",
        "shap.summary_plot(shap_values_positive_class, shap_sample, plot_type=\"bar\", show=False)\n",
        "plt.title(\"Mean Absolute SHAP Value by Feature\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
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